Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations.

近几十年来，锐化的图像取证引起了研究人员的极大兴趣。最先进的技术已经实现了强锐化检测的高精度，而检测弱锐化仍然是一个挑战。提出了一种基于阈值二进制编码的图像锐化检测算法。通过锐化引入的过冲伪像扩大了两个图像像素和不清晰的蒙版元素的局部最大值和最小值之间的差异，基于此阈值局部二值模式运算符用于捕获锐化的轨迹。然后根据旋转对称不变性和纹理类型对图案进行编码。从编码图案的统计分布中提取特征，并将其馈入分类器以进行锐化检测。在实践中，针对轻型和离线应用分别构建了两个分类器，一个是具有182个特征的单个Fisher线性判别式（FLD），另一个是具有5460个特征的整体分类器（EC）。在BOSS，NRCS和RAISE数据集上的实验结果表明，对于弱锐化检测，FLD在EPTC，EPBC和LBP功能方面优于CNN和SVM，并且将EC与TBCs功能一起使用可进一步改善性能，从而获得比EC更好的结果。具有TLBP和SRM功能。此外，该算法对JPEG后压缩和噪声添加具有鲁棒性，并且可以将锐化与其他操作区分开。另一个是具有5460功能的集成分类器（EC）。在BOSS，NRCS和RAISE数据集上的实验结果表明，对于弱锐化检测，FLD在EPTC，EPBC和LBP功能方面优于CNN和SVM，并且将EC与TBCs功能一起使用可进一步改善性能，从而获得比EC更好的结果。具有TLBP和SRM功能。此外，该算法对JPEG后压缩和噪声添加具有鲁棒性，并且可以将锐化与其他操作区分开。另一个是具有5460功能的集成分类器（EC）。在BOSS，NRCS和RAISE数据集上的实验结果表明，对于弱锐化检测，FLD在EPTC，EPBC和LBP功能方面优于CNN和SVM，并且将EC与TBCs功能一起使用可进一步改善性能，从而获得比EC更好的结果。具有TLBP和SRM功能。此外，所提出的算法对于后JPEG压缩和噪声添加具有鲁棒性，并且可以将锐化与其他操作区分开。