Image forgery localization (IFL) is a crucial technique for preventing tampered image misuse and protecting social safety. However, due to the rapid development of image tampering technologies, extracting more comprehensive and accurate forgery clues remains an urgent challenge. To address these challenges, we introduce a novel information-theoretic IFL framework named SUMI-IFL that imposes sufficiency-view and minimality-view constraints on forgery feature representation. First, grounded in the theoretical analysis of mutual information, the sufficiency-view constraint is enforced on the feature extraction network to ensure that the latent forgery feature contains comprehensive forgery clues. Considering that forgery clues obtained from a single aspect alone may be incomplete, we construct the latent forgery feature by integrating several individual forgery features from multiple perspectives. Second, based on the information bottleneck, the minimality-view constraint is imposed on the feature reasoning network to achieve an accurate and concise forgery feature representation that counters the interference of task-unrelated features. Extensive experiments show the superior performance of SUMI-IFL to existing state-of-the-art methods, not only on in-dataset comparisons but also on cross-dataset comparisons.

翻译：图像伪造定位（IFL）是防止篡改图像滥用、保护社会安全的关键技术。然而，由于图像篡改技术的快速发展，提取更全面、更准确的伪造线索仍然是一个紧迫的挑战。为应对这些挑战，我们提出了一种新颖的信息论IFL框架，命名为SUMI-IFL，该框架对伪造特征表示施加了充分性视角和最小性视角的约束。首先，基于互信息的理论分析，我们在特征提取网络中强制执行充分性视角约束，以确保潜在伪造特征包含全面的伪造线索。考虑到仅从单一角度获取的伪造线索可能不完整，我们通过整合来自多个视角的若干独立伪造特征来构建潜在伪造特征。其次，基于信息瓶颈理论，我们在特征推理网络中施加最小性视角约束，以实现准确且简洁的伪造特征表示，以对抗与任务无关特征的干扰。大量实验表明，SUMI-IFL不仅在数据集内比较中，而且在跨数据集比较中，均优于现有的最先进方法。