语义不匹配与感知退化：图像编辑免疫的新视角
Semantic Mismatch and Perceptual Degradation: A New Perspective on Image Editing Immunity
AI总结：
本文提出SIFM方法和ISR度量标准，通过语义不匹配和感知退化来评估图像编辑免疫效果，提升对抗恶意扩散式操纵的能力。
摘要：
文本引导的图像编辑通过扩散模型虽然强大，但引发了关于滥用的重大担忧，推动了通过不可察觉的扰动来免疫图像免受未经授权编辑的努力。现有的评估免疫成功的方法通常依赖于测量受保护图像生成的输出与未受保护原始图像生成的参考输出之间的视觉差异性。这种方法根本上忽略了图像免疫的核心要求，即破坏与攻击者意图的语义对齐，无论是否偏离任何特定输出。我们认为，免疫的成功应通过编辑输出要么在语义上与提示不匹配，或者遭受显著的感知退化来定义，这两种情况都挫败了恶意意图。为了将这一原则具体化，我们提出了协同中间特征操纵（SIFM），一种通过双重协同目标策略性扰动中间扩散特征的方法：（1）最大化特征与原始编辑轨迹的差异以破坏与预期编辑的语义对齐，（2）最小化特征范数以诱导感知退化。此外，我们引入了免疫成功率（ISR），一种新的度量标准，旨在首次严格量化真正的免疫效果。ISR量化了免疫导致与提示相对的语义失败或显著感知退化所占的比例，通过多模态大语言模型（MLLMs）进行评估。广泛的实验显示，我们的SIFM在保护视觉内容免受恶意扩散式操纵方面实现了最先进的性能。
英文摘要：
Text-guided image editing via diffusion models, while powerful, raises significant concerns about misuse, motivating efforts to immunize images against unauthorized edits using imperceptible perturbations. Prevailing metrics for evaluating immunization success typically rely on measuring the visual dissimilarity between the output generated from a protected image and a reference output generated from the unprotected original. This approach fundamentally overlooks the core requirement of image immunization, which is to disrupt semantic alignment with attacker intent, regardless of deviation from any specific output. We argue that immunization success should instead be defined by the edited output either semantically mismatching the prompt or suffering substantial perceptual degradations, both of which thwart malicious intent. To operationalize this principle, we propose Synergistic Intermediate Feature Manipulation (SIFM), a method that strategically perturbs intermediate diffusion features through dual synergistic objectives: (1) maximizing feature divergence from the original edit trajectory to disrupt semantic alignment with the expected edit, and (2) minimizing feature norms to induce perceptual degradations. Furthermore, we introduce the Immunization Success Rate (ISR), a novel metric designed to rigorously quantify true immunization efficacy for the first time. ISR quantifies the proportion of edits where immunization induces either semantic failure relative to the prompt or significant perceptual degradations, assessed via Multimodal Large Language Models (MLLMs). Extensive experiments show our SIFM achieves the state-of-the-art performance for safeguarding visual content against malicious diffusion-based manipulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
