The rapid advancement of generative models has significantly enhanced the realism and customization of digital content creation. The increasing power of these tools, coupled with their ease of access, fuels the creation of photorealistic fake content, termed deepfakes, that raises substantial concerns about their potential misuse. In response, there has been notable progress in developing detection mechanisms to identify content produced by these advanced systems. However, existing methods often struggle to adapt to the continuously evolving landscape of deepfake generation. This paper introduces Prompt2Guard, a novel solution for exemplar-free continual deepfake detection of images, that leverages Vision-Language Models (VLMs) and domain-specific multimodal prompts. Compared to previous VLM-based approaches that are either bounded by prompt selection accuracy or necessitate multiple forward passes, we leverage a prediction ensembling technique with read-only prompts. Read-only prompts do not interact with VLMs internal representation, mitigating the need for multiple forward passes. Thus, we enhance efficiency and accuracy in detecting generated content. Additionally, our method exploits a text-prompt conditioning tailored to deepfake detection, which we demonstrate is beneficial in our setting. We evaluate Prompt2Guard on CDDB-Hard, a continual deepfake detection benchmark composed of five deepfake detection datasets spanning multiple domains and generators, achieving a new state-of-the-art. Additionally, our results underscore the effectiveness of our approach in addressing the challenges posed by continual deepfake detection, paving the way for more robust and adaptable solutions in deepfake detection.

翻译：生成模型的快速发展显著提升了数字内容创作的真实感与可定制性。这些工具日益增强的能力及其易获取性，催生了被称为深度伪造的逼真伪造内容，引发了对其潜在滥用的严重关切。为此，检测机制的开发已取得显著进展，旨在识别由这些先进系统生成的内容。然而，现有方法往往难以适应不断演变的深度伪造生成技术格局。本文提出Prompt2Guard，一种基于视觉-语言模型（VLMs）和领域特定多模态提示的新型无示例持续图像深度伪造检测方案。相较于以往受限于提示选择准确性或需要多次前向传播的VLM方法，我们采用基于只读提示的预测集成技术。只读提示不与VLM内部表征交互，从而减少多次前向传播的需求，显著提升了生成内容检测的效率和准确性。此外，我们的方法利用专为深度伪造检测设计的文本提示条件机制，实验证明该机制在本研究场景中具有显著优势。我们在CDDB-Hard基准上评估Prompt2Guard——该基准由涵盖多领域和多生成器的五个深度伪造检测数据集构成，用于持续深度伪造检测评估——并取得了新的最优性能。实验结果进一步证实了该方法在应对持续深度伪造检测挑战方面的有效性，为开发更鲁棒、适应性更强的深度伪造检测方案开辟了新路径。