论文标题
图像伪造本地化的块级双重JPEG压缩检测
Block-level Double JPEG Compression Detection for Image Forgery Localization
论文作者
论文摘要
由于图像操纵工具的可用性，伪造的图像在当今世界上存在着无处不在的存在。在这封信中，我们提出了一种基于学习的新方法，该方法利用DCT系数直方图和相应的量化步骤大小之间的固有关系，以基于对单个和双重压缩块的检测，不完全解压缩JPEG图像，以区分JPEG图像中的原始区域和锻造区域。与标准的100个量化矩阵相比，我们考虑了在最近的一项研究中收集的1,120个量化矩阵的各种量化矩阵，用于训练，测试和创建逼真的伪造。特别是，我们仔细地设计了针对densenet的输入，该输入具有特定的量化步骤大小和JPEG块的各个直方图的组合。使用此输入来学习压缩工件可产生最先进的结果，以检测单个和双重压缩块的尺寸$ 256 \ times 256 $，并为较小尺寸的$ 128 \ times 128 $和$ 64 \ times 64 $提供更好的结果。因此，在现实的锻造图像上获得了改进的伪造性能。同样，对于训练中使用的矩阵而言，对于用完全不同的量化矩阵压缩的测试块，该方法的表现优于当前的最新方法。
Forged images have a ubiquitous presence in today's world due to ease of availability of image manipulation tools. In this letter, we propose a deep learning-based novel approach which utilizes the inherent relationship between DCT coefficient histograms and corresponding quantization step sizes to distinguish between original and forged regions in a JPEG image, based on the detection of single and double compressed blocks, without fully decompressing the JPEG image. We consider a diverse set of 1,120 quantization matrices collected in a recent study as compared to standard 100 quantization matrices for training, testing, and creating realistic forgeries. In particular, we carefully design the input to DenseNet with a specific combination of quantization step sizes and the respective histograms for a JPEG block. Using this input to learn the compression artifacts produces state-of-the-art results for the detection of single and double compressed blocks of sizes $256 \times 256$ and gives better results for smaller blocks of sizes $128 \times 128$ and $64 \times 64$. Consequently, improved forgery localization performances are obtained on realistic forged images. Also, in the case of test blocks compressed with completely different quantization matrices as compared to matrices used in training, the proposed method outperforms the current state-of-the-art.
