Nearly Solved? Robust Deepfake Detection Requires More than Visual Forensics

TL;DR

Deepfake detection remains challenging due to adversarial attacks, but leveraging semantic features and large visuo-lingual models like GPT-4o can enhance robustness and zero-shot detection capabilities.

Contribution

This work demonstrates the importance of semantic features in deepfake detection and introduces a hybrid approach combining low- and high-level detectors for improved robustness.

Findings

State-of-the-art detectors are vulnerable to adversarial attacks.

Semantic embedding models are less susceptible to black-box perturbations.

Large visuo-lingual models outperform current methods in zero-shot detection.

Abstract

Deepfakes are on the rise, with increased sophistication and prevalence allowing for high-profile social engineering attacks. Detecting them in the wild is therefore important as ever, giving rise to new approaches breaking benchmark records in this task. In line with previous work, we show that recently developed state-of-the-art detectors are susceptible to classical adversarial attacks, even in a highly-realistic black-box setting, putting their usability in question. We argue that crucial 'robust features' of deepfakes are in their higher semantics, and follow that with evidence that a detector based on a semantic embedding model is less susceptible to black-box perturbation attacks. We show that large visuo-lingual models like GPT-4o can perform zero-shot deepfake detection better than current state-of-the-art methods, and introduce a novel attack based on high-level semantic manipulation. Finally, we argue that hybridising low- and high-level detectors can improve adversarial robustness, based on their complementary strengths and weaknesses.