Student Projects

PROJECT PROPOSALS 2022-2023

If you are interested in taking a project in our group, please contact the responsible person under the detailed description of the project that you would like to choose.


Image compression for DNA based storage 

DNA can be used for storage of information the same way the genetic codes of most living entities, including humans, are stored in their DNA. There are several advantages behind such an approach, such as a much higher storage density, long term preservation capability and better energy efficiency. The underlying information in DNA is represented in a quaternary code (AGCT) instad of a binary code (01). This calls for completely new approaches to efficiently code information in a DNA compatible manner. 

The goal of this project is to study alternatives approaches proposed in the state of the art to store informaiton in DNA and to come up with an end-to-end image compression simulator by taking advantage of publicly accessible implementations.  

The following tasks should be performed during the project:

  • Study the relevant state of the art relevant in DNA storage and coding.
  • Identify existing source code for DNA storage and analyse them.
  • Design and implement a simulator of image compression for DNA storage based on state of the art implementations  
  • Analyse the performance of the simulator.

Requirements: Basic knowledge of signal and image processing. Good programming skills.

Contact: Touradj Ebrahimi

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, Digital Humanities, Mechanical Engineering, Micro Engineering, Management of Technology and/or equivalent.

Number of students: One


Non-fungible tokens (NFTs) for management of image assets 

Non-fungible tokens (NFTs) have become a hot topic in digital assets ownership management with a wide tange of applications ranging from the trade of electronic art to micro-licensing of digital assets. One of the most popular types of digital assets in NFT-based applications is image assets. But current practices in NFTs do not take the structure of digital images into account, which results in inefficient and even untrustworthy solutions.  

The goal of this project is to study the state of the art in creation and management of NFTs for image assets, to identify their weaknesses, and to propose solutions and implement them in proof of concept fashion, showing their advantages.  

The following tasks should be performed during the project:

  • Study the relevant state of the art relevant to NFTs and curent best practices.
  • Identify weaknesses of the current solutions.
  • Design and implement solutions to cope with some of the identified weaknesses   
  • Analyse the proposed solutions and compare to state of the art and existing practices.

Requirements: Good programming skills. 

Contact: Touradj Ebrahimi

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, Digital Humanities, Mechanical Engineering, Micro Engineering, Management of Technology and/or equivalent.

Number of students: One


ProCam – Privacy-friendly infrared camera

Many contagious diseases such as Covid19 can induce high temperatures and fever in a significant number of affected individuals. Contact tracing of such individuals and analysis of their behavior and interactions with others and their environment can be a useful tool to contain the spread of contagious diseases. This is in particular useful for back-to-work strategy as well as protection of critical personnel or more vulnerable individuals. Existing solutions are either too expensive (e.g. high-end thermal cameras) or not precise enough (e.g. contact tracing using smartphones). Also, surveillance and analysis of their content pose various ethical challenges, including invasion of privacy.

ProCam is an interdisciplinary project supported by EPFL and federates Bachelor, Master, and Doctoral students of EPFL. Students can elect to ProCam as part of their semester and final projects and receive credits and will be supervised by an EPFL student or researcher.

The objective of ProCam is to design, build and test a new type of connected camera with multiple sensors that can track people while protecting their privacy and at the same time identify those with high body temperature. The device is in form of a kitset made of off-shelf components and open software that will allow anybody to build it at low cost, easily install and start using it. A dedicated server records all captured footage in a secure and anonymized way with the possibility of further analysis, visualization, and eventual de-anonymization. An early prototype of the camera system with an enclosure to protect its components has been created.

The following tasks should be performed during the project:

  • Study the existing ProCam prototype as well as relevant state of the art relevant to the project.
  • With the help of other students update the prototype, implement, and test.
  • Analyze the characteristics of the camera and its strengths. 
  • Propose improvements for next-generation cameras.

Requirements: Because of the interdisciplinary nature of the project any background is welcome.

Contact: Touradj Ebrahimi

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, Digital Humanities, Mechanical Engineering, Micro Engineering, Management of Technology and/or equivalent.

Number of students: Group of students in collaboration with senior researchers


Image de-noising in the latent representation

Deep-learning based image compression algorithms are becoming popular, showing excellent performance in terms of compression efficiency and perceived visual quality. The most popular approach to this problem is through autoencoders, which are neural networks able to map an input image in the pixel domain to a compact representation in a latent space. Consequently, another network reconstructs the original image in the pixel domain from its latent space, as accurately as possible.

Traditionally, image processing algorithms are applied to the reconstructed images in the pixel domain. Recently, researchers are attempting to apply these algorithms in the latent space, i.e., before the decoding, reducing the computational cost while achieving the same performance in accuracy. In particular, this project aims at analyzing the problem of image de-noising in the latent space.

The objective of the project is to first familiarize the student with the most popular deep-learning based image compression methods and with the most popular deep-learning based methods for image denoising. Then the student will explore the problem of image denoising in the latent space, implementing a suitable solution.

The following tasks should be performed during the project:

  • Study the state of the art of deep-learning based image compression followed by identification of the best network to use during the project.
  • Study the state of the art of deep-learning based image denoising followed by identification of the best network to use during the project.
  • Collect a suitable dataset for experiments.
  • Propose and implement a solution by merging the image encoder with the denoising network and training it.
  • Evaluate the above and report the results.

Requirements: Background on image processing and deep-learning. Good skills in Python programming.

Contact: Michela Testolina

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, or equivalent.

Number of students: One


Semantic segmentation in the latent representation

 

Deep-learning based image compression algorithms are becoming popular, showing excellent performance in terms of compression efficiency and perceived visual quality. The most popular approach to this problem is through autoencoders, which are neural networks able to map an input image in the pixel domain to a compact representation in a latent space. Consequently, another network reconstructs the original image in the pixel domain from its latent space, as accurately as possible.

Traditionally, image processing algorithms are applied to the reconstructed images in the pixel domain. Recently, researchers are attempting to apply these algorithms in the latent space, i.e., before the decoding, reducing the computational cost while achieving the same performance in accuracy. In particular, this project aims at analyzing the problem of image semantic segmentation in the latent space.

The objective of the project is to first familiarize the student with the most popular deep-learning based image compression methods and with the most popular deep-learning based methods for image semantic segmentation. Then the student will explore the problem of image semantic segmentation in the latent space, implementing a suitable solution.

The following tasks should be performed during the project:

  • Study the state of the art of deep-learning based image compression followed by identification of the best network to use during the project.
  • Study the state of the art of deep-learning based image semantic segmentation followed by identification of the best network to use during the project.
  • Collect a suitable dataset for experiments.
  • Propose and implement a solution by merging the image encoder with the semantic segmentation network and training it.
  • Evaluate the above and report the results.

Requirements: Background on image processing and deep learning. Good skills in Python programming.

Contact: Michela Testolina

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, or equivalent.

Number of students: One


Optimized joint compression and denoising pipeline for face detection and recognition applications

In the last decade, the number of face detection and recognition applications based on deep-learning has increased rapidly. Anyway, it is well known that deep-learning based methods are sensitive to noise, which could lead to potential misdetection, wrong recognition, or false positives and false negatives. Different types of noise can affect the pictures, i.e. the camera noise, which is mainly visible on pictures captured in low light conditions, and the compression artifacts, introduced by the coding algorithm.

The traditional image processing pipeline usually includes, in cascade, image denoising and compression blocks, applied in the pixel domain. Recently, researchers are attempting to combine these two blocks, integrating the denoising operations in learning-based compression methods, and therefore indirectly applying them to the latent space, i.e., before the decoding. This approach might lead, in fact, to a reduced computational time while achieving similar performance in accuracy. In particular, this project aims at proposing and analyzing a pipeline for combined denoising and compression specifically optimized for face detection/recognition applications.

The objective of this project is to first familiarize the student with the most popular learning-based image compression methods and with the most popular learning-based denoising methods. Then the student will explore different face detection and recognition algorithms. After, the student will explore and propose a solution for unified compression and denoising,  able to maximize the performance of the face detection application.

The following tasks should be performed during the project:

  • Study the state of the art of image compression, image denoising and face detection. Select the most relevant algorithms.
  • Collect a suitable dataset of images.
  • Propose and implement a hybrid compression and denoising solution for face detection application.
  • Evaluate the proposed solution and report the results.

Requirements: Background on image compression, computer vision and deep-learning. Good skills in Python programming.

Contact: Michela Testolina, Yuhang Lu

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, or equivalent.

Number of students: One


Visually lossless quality assessment of compressed images

In the last decades, the number of pictures taken and stored on digital devices has increased exponentially. Most of the photos are acquired using smartphones with the aim of capturing special moments, and stored to revive them in the future. Users usually demand the best possible visual quality when storing their memories. For this reason, researchers are working through visually lossless approaches to image compression, where the difference between the original and the decompressed images cannot be perceived by the human eye. 

To help the research towards such approaches, specific subjective tests have been proposed and standardized. Those methods are anyway known to be expensive and time-consuming. To solve this problem, an objective image quality metric specific for visually lossless image coding should be designed.

The objective of this project is to first familiarize the student with the state of the art for image compression and quality assessment, in particular for visually lossless methods. Then the student will conduct a subjective image quality experiment to collect data on the visibility of the artifacts caused by the compression. Then the student will analyze the data and propose, design and implement a deep-learning based image quality metric able to assess whether a compressed image has visually lossless quality.

The following tasks should be performed during the project:

  • Study the state of the art of visually lossless quality metrics.
  • Explore the most popular neural network architectures for objective quality assessment.
  • Collect a dataset of images, suitable for the experiment and compress the images at different target bitrates with multiple compression methods.
  • Conduct a subjective experiment in order to collect the subjective scores.
  • Propose and implement a neural-network based solution for objective visually lossless quality assessment.
  • Evaluate the performance of the proposed solution on the collected dataset.

Requirements: Background on deep-learning image compression and image quality assessment. Good skills in Python programming.

Contact: Michela Testolina

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, or equivalent.

Number of students: One


Deep learning for deepfake detection

Due to the increasing spread of doctored or synthetic contents on the Internet and their impact on the dissemination of fake news over social networks, detecting manipulated content has become a major challenge in both academic and professional communities. Major companies have joined forces to organize challenges with the goal of helping in the process of creating widely accessible tools and solutions to detect malicious modifications of multimedia contents.

One of the most important and recent actions was the Deepfake Detection Challenge organized by Facebook and Microsoft, with the involvement of many academic research groups. The organizers hoped that this challenge would result in new technologies for detecting AI-generated videos which can later be used on social networking platforms and/or by journalists. This illustrates the major concerns of large companies about the danger of AI-assisted content manipulations. 

In this project, we tackle the deepfake detection problem by training several convolutional neural networks (CNNs) in a supervised fashion. Finally, the ensembling of different trained CNNs will be studied.

In particular, two main objectives will be pursued in this project. The first aims at finding existing and publicly available deepfake datasets. The second aims at training deep neural networks using the above datasets for the task of deepfake detection. The following tasks should be performed by the student:

  • Review the state of the art deepfake detection methods 
  • Study the state of the art deepfake creation methods and find/generate their corresponding dataset which can further be used for training of CNNs.
  • Run/Adapt/Create a program to detect deepfake images and videos
  • Investigate the most common performance metrics
  • Assess the performance of the trained models against several datasets
  • Document the code and write a report on the project

Requirements: Good skills in Python programming. Background in deep learning and image processing.

Contact: Touradj Ebrahimi

Group: Prof. Touradj Ebrahimi

Suitable for: Master Semester Project or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, or equivalent.

Number of students: One


Exploring Robust Deepfake Detection Methods

In recent years, face manipulation techniques, in particular with Deepfake methods, have raised great public concerns. The deep learning-based tools and open-source software have simplified the creation of such manipulated content. It is crucial to develop Deepfake detection systems that can automatically and effectively identify manipulated videos and images. Although the performance of current detectors shows a rather high accuracy on well-known datasets, it often drastically degrades when they are tested in non-trivial situations with real-world perturbations. A more robust Deepfake detector therefore is desired. 

The objective of this project is to first familiarize the student with the state-of-the-art deep learning-based Deepfake detection methods and databases. Then the student will explore how to improve network robustness from the following aspects: training strategy, data augmentation, and neural network architecture. The student is also encouraged to explore other influencing factors from different angles. At the end, the student will evaluate an improved detection method with a more realistic Deepfake detection assessment framework.

In general, the following tasks shall be performed by the student:

  • Investigate the state-of-the-art deep learning-based Deepfake detection methods
  • Review popular Deepfake detection databases
  • Explore better architectures or data augmentation techniques to improve the robustness of Deepfake detector
  • Assess the performance of a proposed and trained detector with the improved Deepfake detection assessment framework
  • Document the code and results and write a report on the project

Requirements: Background in image processing and deep learning. Good skills in Python programming.

Contact: Yuhang Lu

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project, or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, and equivalent.

Number of students: One


Identity-Preserving Low-Resolution Face Recognition

Face recognition (FR) has become a key technology in multiple applications. In recent years we have witnessed the great progress of convolutional neural networks (CNNs) in face recognition. Although current deep learning-based face recognition algorithms have achieved very promising performance on public datasets, their performance is heavily degraded when methods are tested with low-resolution face images. This problem is particularly critical in surveillance applications. One of the most straightforward solutions is to properly interpolate the low-resolution faces with super-resolution methods and then perform face recognition. However, such up-sampled data often lacks sufficient identity information for deep models.

The objective of this project is to investigate an identity-preserving low-resolution face recognition system. The student will first investigate the current state-of-the-art in this area as well as the current most popular face recognition pipelines and databases. Moreover, the student is expected to come up with an end-to-end solution for this task.

In general, the following tasks shall be performed by the student:

  • Study the state-of-the-art deep learning-based face recognition algorithms and super-resolution algorithms
  • Investigate or create suitable face datasets and design evaluation protocols for this specific task
  • Establish a generic face recognition pipeline as a baseline
  • Explore an end-to-end low-resolution face recognition solution
  • Document the code and results and write a report on the project

Requirements: Background in image processing and deep learning. Good skills in Python programming.

Contact: Yuhang Lu

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Thesis, Master Semester Project, or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, and equivalent.

Number of students: One


Measuring the Influencing Factors for AI-based Face Recognition

Face recognition (FR) has become a key technology in multiple applications. In recent years we have witnessed the great progress of convolutional neural networks (CNNs) in face recognition. Although a black-box approach based on deep learning can boost the performance, it is hard to understand the decision and, more importantly, how to improve weaknesses. 

The objective of this project is to identify key factors that will influence the face recognition system and to provide a reasonable description of underlying mechanisms. The student is expected to investigate possible influencing factors, including but not limited to data quality-related factors, human-related factors, and deep-model related factors. The student will study at least one type of influencing factor and create evaluation metrics and protocols, which should quantitatively demonstrate the impact of such a factor. Afterward, the student is to provide insights on how to understand and explain the decision made by the system.

In general, the following tasks shall be performed by the student:

  • Study the state-of-the-art deep learning-based face recognition methods and popular databases
  • Design and implement a face recognition pipeline
  • Investigate a number of selected influencing factors and design evaluation protocols to measure the impact on the selected face recognition pipeline
  • Investigate the rationale behind decisions made by the FR system based on experiments
  • Document the code and results and write a report on the project

Requirements: Background in image processing and deep learning. Good skills in Python programming.

Contact: Yuhang Lu

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project, or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, and equivalent.

Number of students: More than One


Point cloud compression using deep neural networks


Point cloud imaging has recently emerged as a viable solution for immersive 3D content representation in augmented, mixed and virtual reality applications. The vast amount of data, though, needed to faithfully reproduce real-world sceneries with this type of imaging makes inevitable the demand for efficient compression solution.

Visual data compression typically comes at the expense of distortions and the presence of artifacts that affect the visual quality of the compressed models. Thus, it is of crucial importance to not only reduce the amount of data needed to represent a model, but also to maintain the highest possible visual quality. The majority of point cloud compression schemes are currently based either on efficient geometrical data structures, or on projection-based solutions. Recently, deep convolutional neural networks have been proposed for point cloud compression purposes, showing remarkable performance gains with respect to the alternative solutions. Following the current trends, the objective of this project is to design and implement a deep neural network module for point cloud compression. The network should be able to efficiently compress point cloud models at various bitrates, while maintaining the highest possible visual quality. The interested reader can refer to [1] for an illustration of a similar study.

The following tasks should be performed during the project:

  • Study the state-of-the-art in deep neural network for point cloud representations.
  • Design and implement a deep neural network module for point cloud compression.
  • Identify suitable point cloud models for training.
  • Train the network and obtain the network parameters.
  • Quality assessment of the performance of the network.
  • Document all the development process and source code.

Requirements: Good background on image processing and machine learning. Good skills in programming.

Contact: Davi Lazzarotto

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, or equivalent.

Number of students: One.

[1] E. Alexiou; K. Tung; T. Ebrahimi : Towards neural network approaches for point cloud compression. 2020. SPIE Optical Engineering + Applications, Online, August 24-28, 2020.


Point cloud objective quality evaluation


Recent trends show that 3D imaging technologies will dominate the market in the near future. Among the alternatives, point clouds denote a viable solution that has recently emerged for immersive content representation, proven by the current activities of JPEG and MPEG standardization committees. Yet, one of the open problems in this emerging field is the assessment of quality of models under typical degradations. In this project, the task is to investigate and propose new, point-based objective quality metrics for point cloud models.

In essence, a point cloud can be defined as a collection of 3D points in space representing the external surface of an object. Each sample is defined by its position, while associated attributes may also be used in conjunction, in order to provide further information (e.g., color, normal vectors). The set of points that represent the 3D model can be interpreted as a (generally) irregularly sub-sampled surface. To quantify the degradation of a distorted model with respect to a reference, similarities between local surface approximations may be used. In this project, we aim to investigate whether further improvements can be obtained by enabling more informative statistics, better surface approximations, or surface description quantities, and/or other domains to represent the signal (e.g., graphs).

The following tasks should be performed during the project:

  • Study the state-of-the-art in objective quality assessment of point clouds.
  • Analyze alternatives to improve performance of current solutions.
  • Propose and implement an algorithm.
  • Benchmark the proposed algorithm.
  • Document all the development process and source code.

Requirements: Good analytic skills. Good background in image and video processing, or machine learning is required.

Contact: Davi Lazzarotto

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, or equivalent.

Number of students: One.


Point cloud dataset generation


Point clouds have recently drawn a significant amount of interest from academic and industrial partners, as a suitable visual data format to represent 3D contents. In visual data representations, the existence of high-performing objective quality metrics is crucial, to accurately measure the level of visual degradations that are perceived from human observers in the presence of artifacts, commonly introduced due to compression or transmission. For this purpose, reliable objective quality metrics are required. The reliability of objective quality metrics is evaluated in benchmarking studies, where subjective quality scores are considered as the ground truth. The subjective scores are provided by human observers that participate in experiments and rate the visual quality of distorted models.

In this project, the objective is to create a dataset of subjectively annotated point cloud contents under realistic types of distortions. In particular, a set of point clouds will be collected, or generated, and will be distorted using several types of degradations. The degraded models will be evaluated by human observers in a large-scale subjective experiment that will be conducted using an evaluation platform of selection. The generated and collected data will be assembled and released to facilitate further research on the field. The student will acquire basic knowledge on each processing stage that takes place in point cloud representations from acquisition to rendering, with a particular focus on subjective quality evaluation methodologies.

The following tasks should be performed during the project:

  • Acquire knowledge on point cloud acquisition, compression, and rendering.
  • Study the state-of-the-art in subjective and objective quality assessment.
  • Collect, or generate a set of point cloud contents.
  • Degrade the models by applying realistic types of distortions.
  • Get familiarized with a subjective evaluation testbed of selection.
  • Conduct a large-scale experiment to obtain subjective scores for the distorted models.
  • Document all the development process and source code.

Requirements: Good communication skills. Good analytic skills.

Contact: Davi Lazzarotto

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project, or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, or equivalent.

Number of students: One.


Point cloud classification on the compressed domain


The use of point clouds is continuously increasing as an imaging modality for 3D content, stimulated by important use cases such as Virtual Reality and Autonomous Driving. Due to the high amount of data needed for their representation, efficient compression methods have been proposed in the literature, with learning-based methods achieving competitive performance. These solutions open the possibility for performing computer vision tasks such as point cloud classification directly on the compressed bitstream rather than on the distorted decompressed model. Early studies on 2D images have already demonstrated that this technique can achieve superior performance in certain situations, but it is still unclear whether these conclusions would still hold for point clouds.

The goal of this project is to adapt learning-based algorithms for point cloud classification to operate directly on the compressed domain. The student will explore whether this technique reduces or improve the accuracy of the computer vision task. The implemented methods will be assessed by comparing their performance when applied both to the compressed domain and to the distorted decompressed point cloud models.

The following tasks should be performed during the project:

  • Study the state of the art on learning-based point cloud compression methods.
  • Study the state of the art on point cloud classification algorithms.
  • Select a point cloud compression method and a dataset for point cloud classification. 
  • Adapt a point cloud classification algorithm from the literature to operate on the compressed domain.
  • Assess the performance of the algorithm at different compression levels.
  • Document all the development process and source code.

Requirements: Background on deep learning and image processing. Good skills in programming.

Contact: Davi Lazzarotto

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project, or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, or equivalent.

Number of students: One.


Point cloud super resolution on the compressed domain


The use of point clouds is continuously increasing as an imaging modality for 3D content, stimulated by important use cases such as Virtual Reality and Autonomous Driving. Due to the high amount of data needed for their representation, efficient compression methods have been proposed in the literature, with learning-based method achieving competitive performance. These solutions open the possibility for performing 3D processing tasks such as point cloud super resolution directly on the compressed bitstream rather than on the distorted decompressed models. Early studies on 2D images have already demonstrated that this technique can achieve superior performance in certain situations, but it is still unclear whether these conclusions would still hold for point clouds.

The goal of this project is to adapt learning-based algorithms for point cloud super resolution to operate directly on the compressed domain. The student will explore whether this technique reduces or improve the performance of the 3D processing task. The implemented methods will be assessed by comparing their performance when applied both to the compressed domain and to the distorted decompressed point cloud models.

The following tasks should be performed during the project:

  • Study the state of the art on learning-based point cloud compression methods.
  • Study the state of the art on point cloud super resolution algorithms.
  • Select a point cloud compression method and a dataset for point cloud super resolution. 
  • Adapt a point cloud super resolution algorithm from the literature to operate on the compressed domain.
  • Assess the performance of the algorithm at different compression levels.
  • Document all the development process and source code.

Requirements: Background on deep learning and image processing. Good skills in programming.

Contact: Davi Lazzarotto

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project, Master Semester Project, or Master Thesis in Electrical Engineering, Communication Systems, Computer Science, or equivalent.

Number of students: One.


Assessment Framework for Deepfake Detection Methods

In recent years, face manipulation techniques, in particular with Deepfake methods, have raised great public concerns. The deep learning-based tools and open-source software have simplified the creation of such manipulated content. It is crucial to develop Deepfake detection systems that can automatically and effectively identify manipulated videos and images. While the performance of such detectors achieves promising results, they are often assessed in trivial scenarios. A more comprehensive evaluation framework for forgery detection is desired.

The objective of this project is to first familiarize the student with the state-of-the-art deep learning based Deepfake detection methods and databases. Then the student will learn to create different evaluation protocols to assess performance of deepfake detectors. The student is also expected to explore any improvement on the existing algorithm to be more robust to different test scenarios.

In general, the following tasks shall be performed by the student:

  • Review the state-of-the-art deep learning-based Deepfake detection methods and popular databases
  • Study the common digital image processing approaches
  • Assess the impact of these processing methods on the performance of Deepfake detectors and summarize the results
  • Improve the Deepfake detection algorithm based on the results.
  • Document the code and results and write a report on the project

Requirements: Background on image processing and deep learning. Good skills in Python programming

Contact: Yuhang LuMichela Testolina

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Project or Master Semester Project in Electrical Engineering, Communication Systems, or Computer Science.

Number of students: One


Mobile App for Privacy Protection on iOS Platform


Recently, public interest in privacy protection has increased dramatically. However, there is a general believe that protection of privacy will restrict online benefits of users. Therefore, protection of privacy in such a way that does not distract online habits of people is needed. This project focuses on visual privacy protection in images. Specifically, the intention of the project is to develop a mobile (iOS platform) application that would be able to obfuscate personal visual information in an image in a secure and recoverable way and share images via online social networks in a secure way.

The following tasks should be performed during the project:

  • Research and review the existing visual privacy protection tools, as well as the way to share and manage secure content in social networks.
  • Design an app on smartphone with iOS operating system. An iPhone will be provided by the lab.
  • Minimal requirements of the app include:
    • Implementation of security processing (e.g. scrambling) for images on the mobile side.
    • Multi-region processing on image using touch screen.
  • Implementation of a simple key management system.

Requirements: Basic knowledge of image processing, good programming skills in Objective-C, experience in iOS development.

Contact: Davi Lazzarotto

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Project or Master Semester Project in Electrical Engineering, Communication Systems, or Computer Science.

Number of students: One.


Mobile App for Privacy Protection on iOS Platform

Recently, public interest in privacy protection has increased dramatically. However, there is a general believe that protection of privacy will restrict online benefits of users. Therefore, protection of privacy in such a way that does not distract online habits of people is needed. This project focuses on visual privacy protection in images. Specifically, the intention of the project is to develop a mobile (Android platform) application that would be able to obfuscate personal visual information in an image in a secure and recoverable way and share images via online social networks in a secure way.

The following tasks should be performed during the project:

  • Research and review the existing visual privacy protection tools, as well as the way to share and manage secure content in social networks.
  • Design an app on smartphone with Android operating system. An Android phone will be provided by the lab.
  • Minimal requirements of the app include:
    • Implementation of security processing (e.g. scrambling) for images on the mobile side.
    • Multi-region processing on image using touch screen.
  • Implementation of a simple key management system.

Requirements: Basic knowledge of image processing, good programming skills in Java, experience in Android development.

Contact: Davi Lazzarotto

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Project or Master Semester Project in Electrical Engineering, Communication Systems, or Computer Science.

Number of students: One.


Privacy Preserving Photo-Sharing Application

The rapid growth of photo sharing through social media raises serious questions related to ownership, privacy and access to shared images. From the user perspective, effective privacy protection tends to impose restrictions on how users share and access pictures, making privacy protection unattractive. To address such issues, MMSPG has developed ProShare, a mobile App through which pictures can be protected, shared and made selectively accessible in a transparent manner while incurring minimal distraction to the user. To date a large effort has been invested in the development and implementation of the ProShare mobile App. Less attention has been directed at the server side realisation of ProShare.

 

The objective of this student project is to enhance the server side implementation of the ProShare service.

The following tasks should be performed during the project:

  • Study and understand the ProShare service and its implementation (both server side and client side)
  • Review server side architecture and compare this to state-of-the-art implementations for similar services
  • Propose modifications and enhancements to the existing server side implementation
  • Implement a migration process allowing to move the ProShare server to a new computing platform
  • Implement robust and reliable session management
  • Propose and implement additional service features
  • Implement back-end tools for the analysis of usage and user statistics

Requirements: Good communications skills. Good understanding of web server technologies including Apache, MySQL and PHP. Good abilities to think at the systems level.

Contact: Davi Lazzarotto

Group: Prof. Touradj Ebrahimi

Suitable for: Bachelor Semester Project in Computer Science, Communications Systems, Electrical Engineering, or equivalent.

Number of students: One