Image Forgery Localization via Guided Noise and Multi-Scale Feature Aggregation
This work addresses the need for more accurate and robust digital forensics tools to detect forged images, especially for small regions, representing an incremental improvement over existing methods.
The paper tackled the problem of feature degradation and poor detection of small forged regions in image forgery localization by proposing a network with guided noise extraction and multi-scale feature aggregation, achieving state-of-the-art performance on five public datasets, particularly for small forged areas.
Image Forgery Localization (IFL) technology aims to detect and locate the forged areas in an image, which is very important in the field of digital forensics. However, existing IFL methods suffer from feature degradation during training using multi-layer convolutions or the self-attention mechanism, and perform poorly in detecting small forged regions and in robustness against post-processing. To tackle these, we propose a guided and multi-scale feature aggregated network for IFL. Spectifically, in order to comprehensively learn the noise feature under different types of forgery, we develop an effective noise extraction module in a guided way. Then, we design a Feature Aggregation Module (FAM) that uses dynamic convolution to adaptively aggregate RGB and noise features over multiple scales. Moreover, we propose an Atrous Residual Pyramid Module (ARPM) to enhance features representation and capture both global and local features using different receptive fields to improve the accuracy and robustness of forgery localization. Expensive experiments on 5 public datasets have shown that our proposed model outperforms several the state-of-the-art methods, specially on small region forged image.