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2025-07-10 更新
CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection
Authors:Binjia Zhou, Hengrui Lou, Lizhe Chen, Haoyuan Li, Dawei Luo, Shuai Chen, Jie Lei, Zunlei Feng, Yijun Bei
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.
随着图像生成技术的快速发展,面部深度伪造技术的广泛出现给安全领域带来了巨大的挑战,从而加剧了对面部深度伪造检测技术的迫切需求。现有的面部伪造检测技术大致可分为两大类:基于视觉的方法和多媒体方法。前者往往对伪造细节的解释不够清晰,而后者融合了视觉和语言模式,更容易出现幻觉问题。为了解决这些缺点,我们引入了一种视觉细节增强自我校正框架,名为CorrDetail,用于可解释的面部伪造检测。CorrDetail经过精心设计,可以在提供错误引导的问题时纠正真实的伪造细节,旨在培养揭示伪造细节的能力,而不是产生幻觉的回应。此外,为了加强其发现的可靠性,还融入了一个视觉精细细节增强模块,为CorrDetail提供更精确的视觉伪造细节。最终,设计了一种融合决策策略,通过视觉信息补偿和模型偏差减少的整合,进一步提高模型在处理极端样本时的辨别能力。实验结果表明,CorrDetail不仅与最新方法相比达到了最先进的性能,而且在准确识别伪造细节方面表现出色,同时表现出强大的泛化能力。
论文及项目相关链接
Summary
人脸识别伪造技术日益普及,对安全领域构成严峻挑战。为应对现有面部伪造检测技术的不足,我们提出了一种基于视觉细节增强的自校正框架CorrDetail,旨在通过提供错误引导的问题来纠正真实的伪造细节,提高揭示伪造细节的能力而非产生幻觉响应。此外,为提高检测结果的可靠性,还融入了视觉精细细节增强模块,为CorrDetail提供更准确的伪造视觉细节。最终设计了一种融合决策策略,进一步提高模型对极端样本的辨识能力,实现模型的视觉信息补偿和偏差减少。实验表明,CorrDetail相较于最新方法达到了领先水平,并擅长准确识别伪造细节,展现出强大的泛化能力。
Key Takeaways
- 面部深度伪造技术的普及对安全领域构成挑战。
- 现有的面部伪造检测技术主要分为视觉方法和多模态方法,但存在缺陷。
- 提出的CorrDetail框架旨在通过错误引导的问题来纠正伪造细节的真实情况。
- CorrDetail框架结合了视觉细节增强的自校正技术,以提高揭示伪造细节的准确性。
- 为提高可靠性,引入了视觉精细细节增强模块。
- 通过融合决策策略,提高了模型对极端样本的辨识能力。
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