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首页> 外文会议>IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference >Forgery Detection and Tampering Localization of Double JPEG Compression Based on First Digit Features of DCT Coefficients and KNR
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Forgery Detection and Tampering Localization of Double JPEG Compression Based on First Digit Features of DCT Coefficients and KNR

机译：基于DCT系数和KNR的第一位数特征的双JPEG压缩伪造检测和篡改定位

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摘要

A337 improved forgery detection and tampering localization method is proposed for double JPEG compression images based on first digit feature of discrete cosine transform (DCT) coefficients and kernel-based nonlinear representor (KNR). First, a to-be-checked JPEG image is divided into overlapping blocks of size$64imes 64$, and the first digit (1∼9) features of alternating current (AC) DCT coefficients at the first nine positions of every$8imes 8$blocks are obtained in each image block, followed by principal component analysis (PCA) transform to form compact features. And then the KNR classifier is used to judge whether the corresponding image block has been re-compressed. Finally, the test results of double compression are used to locate the tampered area of JPEG image. Experimental results show that in comparison with representative algorithms, the improved algorithm achieves better results, and is robust to operations such as rotation, resizing and feathering. Moreover, KNR classifier outperforms classical SVM classifier in recognition effect and efficiency.
机译：基于离散余弦变换（DCT）系数的第一位数特征和基于核的非线性表示（KNR），针对双JPEG压缩图像，提出了一种改进的A337伪造检测和篡改定位方法。首先，将要检查的JPEG图像分成大小重叠的块 $ 64 \乘以64 $ ，以及每个数字的前九个位置的交流（AC）DCT系数的前几位（1〜9）特征 $ 8 \乘以8 $ 在每个图像块中获取块，然后进行主成分分析（PCA）变换以形成紧凑特征。然后，使用KNR分类器来判断相应的图像块是否已经被重新压缩。最后，使用双重压缩的测试结果来定位JPEG图像的篡改区域。实验结果表明，与代表性算法相比，改进算法取得了更好的效果，并且对旋转，调整大小和羽化等操作具有鲁棒性。此外，在识别效果和效率上，KNR分类器优于传统的SVM分类器。

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