Diffusion Deepfake
Chaitali Bhattacharyya Hanxiao Wang Feng Zhang Sungho Kim Xiatian Zhu
Recent progress in generative AI, primarily through diffusion models,
presents significant challenges for real-world deepfake detection. The
increased realism in image details, diverse content, and widespread
accessibility to the general public complicates the identification of these
sophisticated deepfakes. Acknowledging the urgency to address the vulnerability
of current deepfake detectors to this evolving threat, our paper introduces two
extensive deepfake datasets generated by state-of-the-art diffusion models as
other datasets are less diverse and low in quality. Our extensive experiments
also showed that our dataset is more challenging compared to the other face
deepfake datasets. Our strategic dataset creation not only challenge the
deepfake detectors but also sets a new benchmark for more evaluation. Our
comprehensive evaluation reveals the struggle of existing detection methods,
often optimized for specific image domains and manipulations, to effectively
adapt to the intricate nature of diffusion deepfakes, limiting their practical
utility. To address this critical issue, we investigate the impact of enhancing
training data diversity on representative detection methods. This involves
expanding the diversity of both manipulation techniques and image domains. Our
findings underscore that increasing training data diversity results in improved
generalizability. Moreover, we propose a novel momentum difficulty boosting
strategy to tackle the additional challenge posed by training data
heterogeneity. This strategy dynamically assigns appropriate sample weights
based on learning difficulty, enhancing the model's adaptability to both easy
and challenging samples. Extensive experiments on both existing and newly
proposed benchmarks demonstrate that our model optimization approach surpasses
prior alternatives significantly.