Detecting GAN generated Fake Images using Co-occurrence Matrices: Checkpoint 2 Thomas Kim (tkim61), Henry Sowerby (hsowerby), Jacob Makar-Limanov (jmakarli)
*Introduction: * This can be copied from the proposal.
In recent years, Generative Adversarial Networks (GANs) have become an increasingly popular technique to generate and manipulate images. Our goal is to work on an implementation of the model used in this paper. The authors train a model to detect differences between real and GAN-generated images. The authors calculate co-occurrence matrices on images and pass them into a CNN. This boils down into a standard binary classification problem, choosing between real and generated images.
Challenges: What has been the hardest part of the project you’ve encountered so far?
In implementing an existing paper, it is challenging to nail down the exact implementation of the paper, especially when the paper doesn’t include code, as there are a number of things that seem straightforward but are actually difficult to implement yourself. With respect to the CNN, the paper mentions the layers they used, however does not detail what the parameters for its max pooling layers are-- so it’s just another added complexity of figuring out the right ones. The paper was also vague about co-occurrence matrices. The authors mention that they used this method, but they do not explain fully what a co-occurrence matrix is and how to calculate it. To implement this, further reading from the papers' citations and the internet was needed. Further, similar to how the author’s were unclear about max pooling parameters, it is unclear if the author’s of the paper used a specific co-occurrence filter or had the model learn a co-occurrence filter.
Insights: Are there any concrete results you can show at this point? How is your model performing compared with expectations?
Because we have not fully trained the model, we don’t have any complete results, nor do we have plots to display. However, our CNN model and our method for finding co-occurrence matrices are implemented, and tentative results suggest that these implementations are working.
Plan: Are you on track with your project? What do you need to dedicate more time to? What are you thinking of changing, if anything?
Overall, we are on track with our project-- our CNN model and co-occurrence methods are implemented, and seem to do okay with very small amounts of data. Now we need to dedicate our time to fully training the model.
Data: Successfully downloaded StarGan, and biggan can be accessed from TF's website directly.
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