通过引导噪声与多尺度融合，提升小区域伪造图像定位精度。
Image Forgery Localization via Guided Noise and Multi-Scale Feature Aggregation
- 引入引导式噪声提取模块，全面学习多种伪造下的噪声特征。
- 设计动态卷积的多尺度特征聚合模块，增强小区域伪造检测能力。
- 适用于数字取证领域，尤其适合处理经后处理的伪造图像。
图像伪造定位（IFL）技术旨在检测并定位图像中的伪造区域，对数字取证至关重要。然而，现有方法在多层卷积或自注意力机制训练中易出现特征退化，且对小区域伪造检测效果差，对后处理缺乏鲁棒性。为此，本文提出一种引导式多尺度特征聚合网络。首先，设计一种引导式噪声提取模块，以全面学习不同伪造类型下的噪声特征；其次，提出特征聚合模块（FAM），利用动态卷积在多尺度上自适应融合RGB与噪声特征；此外，引入空洞残差金字塔模块（ARPM），通过不同感受野捕捉全局与局部特征，提升定位精度与鲁棒性。在5个公开数据集上的大量实验表明，所提模型优于多个当前最优方法，尤其在小区域伪造图像上表现突出。
原文摘要 · Abstract (English)
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.
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