Johnny Fernandes
61 lines
5.1 KiB
Markdown
61 lines
5.1 KiB
Markdown
# Deep and Reinforcement Learning (2025/2026 — M.IA003), FEUP/FCUP
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## Deep Learning Project
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**Submission deadline:** May 15th, 2026
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This work will need to be submitted using the Moodle platform. It will be developed during practical classes, but it is expected that the students will complement this work using extra-class hours.
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---
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## 1. Objective
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The objective of this work is to develop deep learning discriminative and generative models, applied to the context of “deep fakes”. The discriminative models will be designed to classify images as “real” vs. “fake”, whereas the generative models will be trained to produce new “fake” examples.
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## 2. Dataset
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The data that you will be using belongs to the DeepFakeFace (DFF) dataset. You can access the dataset files and description via the Hugging Face link. In addition, you can find a detailed description of the dataset in this paper.
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The dataset was generated to assess the ability of deepfake detectors to distinguish AI-generated and authentic images. It contains 30,000 real images of celebrities taken from the IMDB-WIKI dataset. The dataset also contains 90,000 fake images generated with the three following models:
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- Stable Diffusion v1.5
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- Stable Diffusion Inpainting
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- InsightFace
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Each model generated 30,000 fake images.
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## 3. Implementation
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In order to complete this work, you will need to implement two different models:
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1. One classifier, which is trained to distinguish between real and fake images
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2. One generative model, which is trained to create new fake images
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For the first model, you will be free to implement any of the discriminative approaches that will be considered during the theoretical classes (e.g., multilayer perceptrons, convolutional neural networks, visual transformers, etc.).
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For the second model, you will be free to implement any of the generative models that will be considered during the theoretical classes (e.g., generative adversarial networks, variational autoencoders, diffusion models, etc.).
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For both models, you will need to define a proper training strategy as well as the correct way and metrics to evaluate the performance.
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## 4. Project evaluation
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The project will be evaluated by taking in consideration the suitability of the proposed model for the specific task, its correctness, and complexity.
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**VERY IMPORTANT:** The main objective and core of this project will be that of iteratively improving the proposed solutions via a continuous observation of intermediate results and the proposal of adjustments to the algorithm. Projects that simply present a solution without showcasing the evolution of the proposed model will not be considered as sufficient.
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## 5. Submission of the solution
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Your project must be delivered in Moodle by **May 15th, 2026, at 23:59:59**.
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- Final code solution, as a notebook or series of files.
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- Slides for presentation (**pdf format**) focusing on the main issues of the assignment for a 10 minute presentation; any additional information that cannot be presented in that time slot can be included in annexes to the presentation. The presentation should contain the following information:
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- Brief description of the deep learning solutions considered for the problem, both for the discriminative and generative part.
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- **MOST IMPORTANT:** Description of the different implementation steps considered to improve the proposed models: motivate your choices in terms of type of approach, model architecture, training strategy, etc. Show intermediate results, how you interpreted those, and what you decide to change in order to improve the results.
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- Results:
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- Classification performance obtained by the developed discriminative model. Description of the experimental setup, train/val/test splits, and performance metrics chosen.
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- Data generation performance obtained by the developed generative model. Description of the experimental setup and performance metrics chosen.
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- Discussion and conclusions: comments on the performance obtained and final remarks.
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- Filled auto-evaluation file regarding the contribution of each member of the group.
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Further information about the project submission and presentation:
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- The code provided as the solution will need to allow to train the considered models and reproduce the results that you reported. Please do not include dataset files. You can assume I have local access to the dataset.
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- The work must be done by groups of 3 people. Groups formed by less than 3 people must be justified and approved before starting working.
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- Delays in the submission will incur in a grade penalization and eventually in not accepting the work.
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- All works must be presented on May 22nd and 29th, during the practical classes. All group members must be present during the demonstration. If a member of the group is not present to the work presentation, he will receive a zero grade for this work, thus implying failing to pass.
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- Each member of the group must comment on their contribution to the work, and must know what the other members of the group have done. Failing to describe in details what your solution is doing and why will determine a penalization in the overall evaluation of the project.
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