用对比学习检测深度伪造艺术的版权侵权，效果优于现有模型。
DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art
- 设计对比学习框架，区分原创与伪造艺术作品
- 在四种攻击类型下均实现高检测准确率，超越主流预训练模型
- 适合图像版权保护、AI生成内容监管等场景
生成式AI在视觉内容创作中的普及，尤其是艺术领域的应用，引发了严重的版权侵权与伪造问题。大规模训练数据集常混合受版权保护与非受版权保护的艺术作品。由于生成模型容易记忆训练模式，存在不同程度的版权违规风险。本文基于最近提出的DeepfakeArt Challenge基准，提出DFA-CON，一种用于检测版权侵权或伪造的AI生成艺术的对比学习框架。该方法在对比学习框架下构建判别性表征空间，强化原始作品与其伪造版本之间的关联。模型在多种攻击类型（包括修补、风格迁移、对抗扰动、剪切拼接）下进行训练。评估结果表明，该方法在多数攻击类型下均表现出鲁棒的检测性能，优于近期的预训练基础模型。代码与模型检查点将在论文被接收后公开发布。
原文摘要 · Abstract (English)
Recent proliferation of generative AI tools for visual content creation-particularly in the context of visual artworks-has raised serious concerns about copyright infringement and forgery. The large-scale datasets used to train these models often contain a mixture of copyrighted and non-copyrighted artworks. Given the tendency of generative models to memorize training patterns, they are susceptible to varying degrees of copyright violation. Building on the recently proposed DeepfakeArt Challenge benchmark, this work introduces DFA-CON, a contrastive learning framework designed to detect copyright-infringing or forged AI-generated art. DFA-CON learns a discriminative representation space, posing affinity among original artworks and their forged counterparts within a contrastive learning framework. The model is trained across multiple attack types, including inpainting, style transfer, adversarial perturbation, and cutmix. Evaluation results demonstrate robust detection performance across most attack types, outperforming recent pretrained foundation models. Code and model checkpoints will be released publicly upon acceptance.
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