UNMATCH：面向取证图像-声明验证的选择性不平衡令牌-补丁匹配
UNMATCH: Selective Unbalanced Token-Patch Matching for Forensic Image-Claim Verification
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中文总结 AI 辅助
UNMATCH通过方向性多尺度覆盖表示局部图像-声明亲和力，实现轻量级全局-局部分类器，在Fauxtography基准上超越MCOT，并验证了配对破坏对检测的影响。
中文摘要 AI 辅助
上下文图像误用将图像与误导性声明配对。我们研究事实核查配对中的图像-声明对应关系，这些配对包含上下文外重用、视觉操纵或两者兼有。现有的基于配对的检测器通常将两种模态压缩为全局兼容性得分，或学习高度灵活的交互模块，这可能会掩盖决定性的局部不匹配。我们引入方向性多尺度覆盖，这是一种紧凑表示，在三个空间尺度上双向总结局部图像-声明亲和力。在每个尺度上，每个方向由其均值、下四分位数和两个阈值化支持比率总结；方向均值之间的符号差异完成九维尺度描述符。将三个尺度连接起来产生用于轻量级全局-局部分类器的紧凑局部表示。在Fauxtography基准的Snopes子集上进行泄漏感知的三折、三种子评估下，UNMATCH实现了69.82的宏F1和71.05的平衡准确率，分别超过MCOT改编2.60和2.16个百分点。一项匹配重分配干预表明，打破观察到的配对会降低覆盖度，并增加差异和假配对概率。
英文摘要
Contextual image misuse pairs an image with a misleading claim. We study image-claim correspondence in fact-checked pairs containing out-of-context reuse, visual manipulation, or both. Existing pair-based detectors often compress the two modalities into a global compatibility score or learn a highly flexible interaction module, which can obscure a decisive local mismatch. We introduce directional multiscale coverage, a compact representation that summarizes local image-claim affinity in both directions and at three spatial scales. At each scale, each direction is summarized by its mean, lower quartile, and two thresholded support ratios; the signed difference between directional means completes a nine-dimensional scale descriptor. Concatenating the three scales yields a compact local representation for a lightweight global-local classifier. Under leakage-aware three-fold, three-seed evaluation on the Snopes subset of the Fauxtography benchmark, UNMATCH achieves 69.82 Macro-F1 and 71.05 balanced accuracy, exceeding the MCOT adaptation by 2.60 and 2.16 points. A matched-reassigned intervention shows that breaking the observed pairing lowers coverage and increases both discrepancy and false-pair probability.
