LLMs Are Not Yet Ready for Deepfake Image Detection

TL;DR

This paper evaluates the capabilities of vision-language models in zero-shot deepfake detection, revealing their limitations in reliability but highlighting their potential to support human analysts through interpretability and contextual reasoning.

Contribution

It provides a structured zero-shot assessment of four prominent VLMs on multiple deepfake types, identifying their strengths and failure modes in detection tasks.

Findings

VLMs can generate explanations but lack consistent detection accuracy.

Models are vulnerable to stylistic and misleading visual cues.

VLMs may enhance forensic workflows when combined with human expertise.

Abstract

The growing sophistication of deepfakes presents substantial challenges to the integrity of media and the preservation of public trust. Concurrently, vision-language models (VLMs), large language models enhanced with visual reasoning capabilities, have emerged as promising tools across various domains, sparking interest in their applicability to deepfake detection. This study conducts a structured zero-shot evaluation of four prominent VLMs: ChatGPT, Claude, Gemini, and Grok, focusing on three primary deepfake types: faceswap, reenactment, and synthetic generation. Leveraging a meticulously assembled benchmark comprising authentic and manipulated images from diverse sources, we evaluate each model's classification accuracy and reasoning depth. Our analysis indicates that while VLMs can produce coherent explanations and detect surface-level anomalies, they are not yet dependable as standalone detection systems. We highlight critical failure modes, such as an overemphasis on stylistic elements and vulnerability to misleading visual patterns like vintage aesthetics. Nevertheless, VLMs exhibit strengths in interpretability and contextual analysis, suggesting their potential to augment human expertise in forensic workflows. These insights imply that although general-purpose models currently lack the reliability needed for autonomous deepfake detection, they hold promise as integral components in hybrid or human-in-the-loop detection frameworks.