NeXT-IMDL: Build Benchmark for NeXT-Generation Image Manipulation Detection & Localization

• URL: http://arxiv.org/abs/2512.23374v1
• Date: Mon, 29 Dec 2025 11:09:35 GMT
• Title: NeXT-IMDL: Build Benchmark for NeXT-Generation Image Manipulation Detection & Localization
• Authors: Yifei Li, Haoyuan He, Yu Zheng, Bingyao Yu, Wenzhao Zheng, Lei Chen, Jie Zhou, Jiwen Lu,
• Abstract summary: NeXT-IMDL is a large-scale diagnostic benchmark designed to probe the boundaries of current detectors.<n>NeXT-IMDL categorizes AIGC-based manipulations along four fundamental axes: editing models, manipulation types, content semantics, and forgery.<n>Our experiments on 11 representative models reveal a critical insight: while these models perform well in their original settings, they exhibit systemic failures and significant performance degradation.
• Score: 67.84497768987023
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The accessibility surge and abuse risks of user-friendly image editing models have created an urgent need for generalizable, up-to-date methods for Image Manipulation Detection and Localization (IMDL). Current IMDL research typically uses cross-dataset evaluation, where models trained on one benchmark are tested on others. However, this simplified evaluation approach conceals the fragility of existing methods when handling diverse AI-generated content, leading to misleading impressions of progress. This paper challenges this illusion by proposing NeXT-IMDL, a large-scale diagnostic benchmark designed not just to collect data, but to probe the generalization boundaries of current detectors systematically. Specifically, NeXT-IMDL categorizes AIGC-based manipulations along four fundamental axes: editing models, manipulation types, content semantics, and forgery granularity. Built upon this, NeXT-IMDL implements five rigorous cross-dimension evaluation protocols. Our extensive experiments on 11 representative models reveal a critical insight: while these models perform well in their original settings, they exhibit systemic failures and significant performance degradation when evaluated under our designed protocols that simulate real-world, various generalization scenarios. By providing this diagnostic toolkit and the new findings, we aim to advance the development towards building truly robust, next-generation IMDL models.

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