Explainable Deepfake Detection with Feature-robust Augmentation and Evidence-grounded Explanation Optimization

2026-08-21

AI Summary

本文针对深度伪造检测的解释性不足和图像质量下降导致的问题，提出了一种结合特征鲁棒增强与证据导向解释优化的新框架。

Abstract

Explainable deepfake detection extends binary classification by requiring models to not only predict authenticity but also provide interpretable justifications. This expanded scope is critical in practice, where users like forensic analysts need insight into the rationale behind the detection. Despite advancements, current approaches suffer from two critical deficiencies: (1)vulnerability to image quality degradation: detection accuracy plummets on low-quality samples, while naive augmentation strategies may induce feature drift and impair performance as diversity expands. (2) factually flawed explanations: explanation models may omit manipulation evidence or hallucinate irrelevant details, undermining interpretability. To address it, we propose a framework with two innovations. For robust deepfake detection, we introduce Feature-robust Augmentation, which comprises diversified degradation-aware augmentation strategies, and a supervised contrastive learning pattern paired with a mean-teacher architecture that stabilizes features against augmentations through consistency constraints. For explanation, we devise an evidence-grounded preference optimization process that guides model to prioritize genuine manipulation traces by learning from chosen-rejected explanation pairs, where rejected samples are constructed via evidence omission or irrelevant information injection. The proposed approach wins the first place in ACM Multimedia 2026 Explainable Deepfake Detection Challenge.The code is available at https://github.com/oceanflowlab/EDD.git.

Problem

• Explainable Deepfake Detection
• Image Quality Degradation
• Feature Drift
• Factually Flawed Explanations
• Manipulation Evidence

Innovation

• Feature-robust Augmentation
• Evidence-grounded Explanation Optimization
• Supervised Contrastive Learning
• Mean-teacher Architecture