A Multi-Domain Feature Fusion Framework for Generalizable Deepfake Detection Across Different Generators
arXiv:2606.14230v1 Announce Type: cross Abstract: Deepfakes are artificially generated images, audio, or videos that threaten privacy, security, and information integrity. Detecting such content is crucial for countering disinformation, as the latest models generate highly realistic content. While spatial- or frequency-based approaches achieve good detection rates on Generative Adversarial Networks (GANs)-based generated deepfakes, they often struggle with recent diffusion model-generated images. In particular, existing approaches rarely exploit complementary multi-domain representations or sy
The proliferation of advanced deepfake generation models, particularly diffusion models, necessitates more robust and generalizable detection methods to counteract evolving disinformation threats.
Sophisticated deepfake detection is critical for maintaining information integrity and countering increasingly realistic deceptive content that can undermine trust in media and institutions.
This framework offers a path toward more resilient deepfake detection systems that can adapt to new generation techniques, moving beyond current limitations of existing spatial and frequency-based approaches.
- · Cybersecurity firms
- · Digital forensics
- · Social media platforms
- · Intelligence agencies
- · Deepfake creators
- · Disinformation networks
- · Unprepared social platforms
Improved deepfake detection capabilities can mitigate the immediate impact of synthetic media.
Greater trust in digital content could be restored, reducing the effectiveness of specific forms of online manipulation.
The arms race between deepfake generation and detection may push AI ethics and regulation into more prominent policy discussions.
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Read at arXiv cs.CL