Beyond Semantic Priors: Mitigating Optimization Collapse for Generalizable Visual Forensics

TL;DR

This paper identifies a failure mode in deepfake detectors trained with SAM, introduces theoretical tools to analyze it, and proposes CoRIT, a new model that mitigates this issue and improves generalization in forgery detection.

Contribution

The work formalizes Optimization Collapse in deepfake detection, links it to intrinsic generalization limits, and introduces CoRIT, a novel model with strategies to enhance robustness and generalization.

Findings

CoRIT outperforms existing methods on cross-domain benchmarks.

Optimization Collapse is linked to layer-wise GSNR attenuation.

Theoretical analysis connects COR, GSNR, and stability in SAM training.

Abstract

While Vision-Language Models (VLMs) like CLIP have emerged as a dominant paradigm for generalizable deepfake detection, a representational disconnect remains: their semantic-centric pre-training is ill-suited for capturing non-semantic artifacts inherent to hyper-realistic synthesis. In this work, we identify a failure mode termed Optimization Collapse, where detectors trained with Sharpness-Aware Minimization (SAM) degenerate to random guessing on non-semantic forgeries once the perturbation radius exceeds a narrow threshold. To theoretically formalize this collapse, we propose the Critical Optimization Radius (COR) to quantify the geometric stability of the optimization landscape, and leverage the Gradient Signal-to-Noise Ratio (GSNR) to measure generalization potential. We establish a theorem proving that COR increases monotonically with GSNR, thereby revealing that the geometric instability of SAM optimization originates from degraded intrinsic generalization potential. This result identifies the layer-wise attenuation of GSNR as the root cause of Optimization Collapse in detecting non-semantic forgeries. Although naively reducing perturbation radius yields stable convergence under SAM, it merely treats the symptom without mitigating the intrinsic generalization degradation, necessitating enhanced gradient fidelity. Building on this insight, we propose the Contrastive Regional Injection Transformer (CoRIT), which integrates a computationally efficient Contrastive Gradient Proxy (CGP) with three training-free strategies: Region Refinement Mask to suppress CGP variance, Regional Signal Injection to preserve CGP magnitude, and Hierarchical Representation Integration to attain more generalizable representations. Extensive experiments demonstrate that CoRIT mitigates optimization collapse and achieves state-of-the-art generalization across cross-domain and universal forgery benchmarks.