VCE: Safe Autoregressive Image Generation via Visual Contrast Exploitation

• URL: http://arxiv.org/abs/2509.16986v1
• Date: Sun, 21 Sep 2025 09:00:27 GMT
• Title: VCE: Safe Autoregressive Image Generation via Visual Contrast Exploitation
• Authors: Feng Han, Chao Gong, Zhipeng Wei, Jingjing Chen, Yu-Gang Jiang,
• Abstract summary: Methods to safeguard autoregressive text-to-image models remain underexplored.<n>We propose Visual Contrast Exploitation (VCE), a novel framework that precisely decouples unsafe concepts from their associated content semantics.<n>Our experiments demonstrate that our method effectively secures the model, achieving state-of-the-art results while erasing unsafe concepts and maintaining the integrity of unrelated safe concepts.
• Score: 57.36681904639463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, autoregressive image generation models have wowed audiences with their remarkable capability in creating surprisingly realistic images. Models such as GPT-4o and LlamaGen can not only produce images that faithfully mimic renowned artistic styles like Ghibli, Van Gogh, or Picasso, but also potentially generate Not-Safe-For-Work (NSFW) content, raising significant concerns regarding copyright infringement and ethical use. Despite these concerns, methods to safeguard autoregressive text-to-image models remain underexplored. Previous concept erasure methods, primarily designed for diffusion models that operate in denoising latent space, are not directly applicable to autoregressive models that generate images token by token. To address this critical gap, we propose Visual Contrast Exploitation (VCE), a novel framework comprising: (1) an innovative contrastive image pair construction paradigm that precisely decouples unsafe concepts from their associated content semantics, and (2) a sophisticated DPO-based training approach that enhances the model's ability to identify and leverage visual contrastive features from image pairs, enabling precise concept erasure. Our comprehensive experiments across three challenging tasks-artist style erasure, explicit content erasure, and object removal-demonstrate that our method effectively secures the model, achieving state-of-the-art results while erasing unsafe concepts and maintaining the integrity of unrelated safe concepts. The code and models are available at https://github.com/Maplebb/VCE.

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