Summary of On the Exploitation Of Dct-traces in the Generative-ai Domain, by Orazio Pontorno (1) et al.
On the Exploitation of DCT-Traces in the Generative-AI Domain
by Orazio Pontorno, Luca Guarnera, Sebastiano Battiato
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deepfake detection is a significant challenge in cybersecurity and digital forensics. Recent generative AI-based solutions can create high-quality synthetic data, making it difficult to identify deepfakes. However, most generative models leave behind unique traces that can be exploited to improve existing detectors’ generalization limitations. This paper analyzes deepfake images generated by GAN and Diffusion Model engines in the frequency domain using Discrete Cosine Transform (DCT) coefficients. The authors hypothesize the existence of a “discriminative fingerprint” within specific coefficient combinations. Machine learning classifiers were trained on various coefficient combinations to identify these fingerprints, while Explainable AI’s LIME algorithm searched for intrinsic discriminative combinations. A robustness test was performed using JPEG compression to analyze trace persistence. The results reveal that generative models leave behind more persistent and discriminative traces at JPEG attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deepfake detection is a big problem in cybersecurity and digital forensics. Right now, AI can make really good fake pictures and videos that are hard to spot. But what if we could find hidden clues in these fake images? That’s what this research paper does! It looks at how deepfakes are made using special algorithms called GANs and Diffusion Models. Then it uses a special tool called Discrete Cosine Transform (DCT) to analyze the pictures. The idea is that there might be certain patterns or “fingerprints” in these fake images that we can use to detect them better. The paper also tries to figure out if these fingerprints are strong enough to withstand things like compressing the image, which makes it harder to detect. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Gan * Generalization * Machine learning * Synthetic data
