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@inproceedings{MarkoCVWW,

title = {Cross-Dataset Deepfake Detection: Evaluating the Generalization Capabilities of Modern DeepFake Detectors},

author = {Marko Brodarič and Peter Peer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/01/MarkoCVWW24_compressed.pdf},

year  = {2024},

date = {2024-01-31},

booktitle = {Proceedings of the 27th Computer Vision Winter Workshop (CVWW)},

pages = {1-10},

abstract = {Due to the recent advances in generative deep learning, numerous techniques have been proposed in the literature that allow for the creation of so-called deepfakes, i.e., forged facial images commonly used for malicious purposes. These developments have triggered a need for effective deepfake detectors, capable of identifying forged and manipulated imagery as robustly as possible. While a considerable number of detection techniques has been proposed over the years, generalization across a wide spectrum of deepfake-generation techniques still remains an open problem. In this paper, we study a representative set of deepfake generation methods and analyze their performance in a cross-dataset setting with the goal of better understanding the reasons behind the observed generalization performance. To this end, we conduct a comprehensive analysis on the FaceForensics++ dataset and adopt Gradient-weighted Class Activation Mappings (Grad-CAM) to provide insights into the behavior of the evaluated detectors. Since a new class of deepfake generation techniques based on diffusion models recently appeared in the literature, we introduce a new subset of the FaceForensics++ dataset with diffusion-based deepfake and include it in our analysis. The results of our experiments show that most detectors overfit to the specific image artifacts induced by a given deepfake-generation model and mostly focus on local image areas where such artifacts can be expected. Conversely, good generalization appears to be correlated with class activations that cover a broad spatial area and hence capture different image artifacts that appear in various part of the facial region.},

keywords = {data integrity, deepfake, deepfake detection, deepfakes, difussion, face, faceforensics++, media forensics},

pubstate = {published},

tppubtype = {inproceedings}

}