The rapid advancement of image inpainting tools, especially those aimed at removing artifacts, has made digital image manipulation alarmingly accessible. This paper proposes several innovative ideas for detecting inpainting forgeries based on low level noise analysis by combining Dual-Tree Complex Wavelet Transform (DT-CWT) for feature extraction with convolutional neural networks (CNN) for forged area detection and localization, and lastly by employing an innovative combination of texture segmentation with noise variance estimations. The DT-CWT offers significant advantages due to its shift-invariance, enhancing its robustness against subtle manipulations during the inpainting process. Furthermore, its directional selectivity allows for the detection of subtle artifacts introduced by inpainting within specific frequency bands and orientations. Various neural network architectures were evaluated and proposed. Lastly, we propose a fusion detection module that combines texture analysis with noise variance estimation to give the forged area. Our approach was benchmarked against state-of-the-art methods and demonstrated superior performance over all cited alternatives. The training code (with pretrained model weights) as long as the dataset will be available at https://github.com/jmaba/Deep-dual-tree-complex-neural-network-for-image-inpainting-detection

翻译：随着图像修复工具（尤其是旨在消除伪影的工具）的迅猛发展，数字图像篡改已变得令人担忧地易于实现。本文提出了若干基于底层噪声分析的修复伪造检测创新思路：首先结合双树复小波变换（DT-CWT）进行特征提取，并利用卷积神经网络（CNN）实现伪造区域的检测与定位；最后通过纹理分割与噪声方差估计的创新性融合完成检测。DT-CWT因其平移不变性而具有显著优势，这增强了其在修复过程中抵抗细微篡改的鲁棒性。此外，其方向选择性使得能够检测特定频带和方向上由修复引入的细微伪影。本文评估并提出了多种神经网络架构。最后，我们设计了一个融合检测模块，该模块结合纹理分析与噪声方差估计以确定伪造区域。我们的方法在多个先进基准上进行了测试，结果表明其性能优于所有引用的对比方法。训练代码（含预训练模型权重）及数据集将在 https://github.com/jmaba/Deep-dual-tree-complex-neural-network-for-image-inpainting-detection 公开。