arXiv:2605.29092v1 Announce Type: cross Abstract: Current face video forgery detectors use wide or dual-stream backbones. We show that a single, lightweight fusion of two handcrafted cues can achieve higher accuracy with a much smaller model. Based on the Xception baseline model (21.9 million parameters), we build two detectors: LFWS, which adds a 1x1 convolution to combine a low-frequency Wavelet-Denoised Feature (WDF) with a phase-spectrum channel derived from Spatial-Phase Shallow Learning (SPSL), and LFWL, which merges WDF with Local Binary Patterns (LBP) in the same way. This extra module
The proliferation of sophisticated deepfake technology necessitates robust and efficient detection methods, driving continuous innovation in this field.
This development allows for more accurate and resource-efficient detection of video face forgeries, critical for maintaining trust in digital media and security applications.
The ability to achieve higher accuracy in deepfake detection with significantly smaller model sizes implies a potential shift towards more deployable and less computationally intensive solutions.
- · Digital security firms
- · Social media platforms
- · Law enforcement agencies
- · Academic researchers in AI security
- · Deepfake creators
- · Organizations relying on simple detection methods
Improved deepfake detection capabilities will reduce the spread and impact of malicious synthetic media.
This could lead to a 'deepfake arms race' where creators develop new methods to bypass enhanced detection, and detectors evolve in response.
The development of highly efficient detection models might allow for real-time, on-device deepfake detection in everyday applications, increasing digital trust.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG