CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection

TL;DR

CorrDetail is a novel interpretable framework that enhances face forgery detection by correcting and highlighting forgery details, leading to state-of-the-art accuracy and robustness against challenging samples.

Contribution

It introduces a visual detail enhanced self-correction approach with error-guided questioning and a fusion strategy for improved interpretability and detection performance.

Findings

Achieves state-of-the-art detection accuracy.

Effectively uncovers forgery details with high precision.

Demonstrates strong generalization across datasets.

Abstract

With the swift progression of image generation technology, the widespread emergence of facial deepfakes poses significant challenges to the field of security, thus amplifying the urgent need for effective deepfake detection.Existing techniques for face forgery detection can broadly be categorized into two primary groups: visual-based methods and multimodal approaches. The former often lacks clear explanations for forgery details, while the latter, which merges visual and linguistic modalities, is more prone to the issue of hallucinations.To address these shortcomings, we introduce a visual detail enhanced self-correction framework, designated CorrDetail, for interpretable face forgery detection. CorrDetail is meticulously designed to rectify authentic forgery details when provided with error-guided questioning, with the aim of fostering the ability to uncover forgery details rather than yielding hallucinated responses. Additionally, to bolster the reliability of its findings, a visual fine-grained detail enhancement module is incorporated, supplying CorrDetail with more precise visual forgery details. Ultimately, a fusion decision strategy is devised to further augment the model's discriminative capacity in handling extreme samples, through the integration of visual information compensation and model bias reduction.Experimental results demonstrate that CorrDetail not only achieves state-of-the-art performance compared to the latest methodologies but also excels in accurately identifying forged details, all while exhibiting robust generalization capabilities.