Can Image Splicing and Copy-Move Forgery Be Detected by the Same Model? Forensim: An Attention-Based State-Space Approach

TL;DR

Forensim is an attention-based model that jointly detects and localizes both splicing and copy-move forgeries in images, advancing forgery detection with a unified approach and new dataset.

Contribution

It introduces Forensim, a novel attention-based framework capable of joint localization of source and target regions for multiple forgery types, and releases the CMFD-Anything dataset.

Findings

Achieves state-of-the-art results on standard benchmarks.

Supports end-to-end training for precise localization.

Provides a new dataset addressing existing limitations.

Abstract

We introduce Forensim, an attention-based state-space framework for image forgery detection that jointly localizes both manipulated (target) and source regions. Unlike traditional approaches that rely solely on artifact cues to detect spliced or forged areas, Forensim is designed to capture duplication patterns crucial for understanding context. In scenarios such as protest imagery, detecting only the forged region, for example a duplicated act of violence inserted into a peaceful crowd, can mislead interpretation, highlighting the need for joint source-target localization. Forensim outputs three-class masks (pristine, source, target) and supports detection of both splicing and copy-move forgeries within a unified architecture. We propose a visual state-space model that leverages normalized attention maps to identify internal similarities, paired with a region-based block attention module to distinguish manipulated regions. This design enables end-to-end training and precise localization. Forensim achieves state-of-the-art performance on standard benchmarks. We also release CMFD-Anything, a new dataset addressing limitations of existing copy-move forgery datasets.