M²RL-Net: Multi-View and Multi-Level Relation Learning Network for Weakly-Supervised Image Forgery Detection

Abstract

As digital media manipulation becomes increasingly sophisticated, accurately detecting and localizing image forgeries with minimal supervision has become a critical challenge. Existing weakly supervised image forgery detection (W-IFD) methods often rely on convolutional neural networks (CNNs) and limited exploration of internal relationships, leading to poor detection and localization performance with only image-level labels. To address these limitations, we introduce a novel Multi-View and Multi-Level Relation Learning Network (M²RL-Net) for W-IFD. M²RL-Net effectively identifies forged images using only image-level annotations by exploring relationships between different views and hierarchical levels within images. Specifically, M²RL-Net achieves patch-level self-consistency learning (PSL) and feature-level contrastive learning (FCL) across different views, facilitating more generalized self-supervised learning of forgery features. In detail, PSL employs self-supervised learning to distinguish consistent and inconsistent regions within images, enhancing its ability to accurately locate tampered areas. FCL utilizes feature-level self-view and multi-view contrastive learning to differentiate between genuine and tampered image features, thereby improving the recognition of authentic and manipulated content across different views. Extensive experiments on various datasets demonstrate that M²RL-Net outperforms existing weakly-supervised methods in both detection and localization accuracy. This research sets a new benchmark for weakly-supervised image forgery detection and lays a robust foundation for future studies in this field.