Ignoring the Decoy: Exposing and Tackling Forensic Distractions in Image Forgery Localization using Masked Convolutions

Xander Staelens, Peter Lambert, Glenn Van Wallendael, Hannes Mareen; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2026, pp. 924-932

Manipulating images has become increasingly easier due to advanced editing tools, leading to a surge in forged images being shared online, raising the need for robust multimedia forensics. Particularly, image forgery localization (IFL) methods aim to detect and localize image tampering. In this paper, we expose and quantify the impact of forensic distractions on state-of-the-art IFL models. We define distractions as benign visual elements that occur in real-world scenarios, such as logos and visible watermarks. Our analysis, first of its kind, reveals that some recent IFL models (like CAT-Net and TruFor) are highly sensitive to distractions, suffering average performance degradations of 15.06% and 55.65%, respectively. To address this issue, we propose masked convolutions, which enable CNN-based IFL methods to ignore masked regions during inference. Masked convolutions require no retraining, allowing existing models to be adapted seamlessly. Distractions can be manually masked, or automatically through a two-step detection process. Experiments show that our proposed approaches significantly improve robustness against distractions, reducing performance degradation to just 4.15% for CAT-Net and 2.94% for TruFor. This demonstrates our method's potential as a distraction-aware technique to enhance the real-world applicability of IFL models.