使用像素和直方图域CNN的鲁棒对比度增强取证
Robust Contrast Enhancement Forensics Using Pixel and Histogram Domain CNNs
摘要：
对比度增强（CE）取证一直是图像取证界关注的问题.它可以为恢复图像历史和识别篡改图像提供一个有效的工具。虽然已经提出了一些CE取证算法，但它们对JPEG压缩和反取证攻击的鲁棒性仍然不令人满意。为了弥补这一不足，本文首先在像素和灰度直方图域上对CE取证进行了区分度分析。然后，在这两个领域分别提出了两种基于卷积神经网络（P-CNN，H-CNN）的端到端方法，实现了对JPEG压缩和反取证攻击的鲁棒CE取证。实验结果表明，在不需要其他操作的情况下，所提出的方法比现有的CE检测方法具有更好的检测性能，并且在使用JPEG预压缩和抗foresics攻击的情况下性能相当。
英文摘要：
Contrast enhancement (CE) forensics has always been ofconcern to image forensics community. It can provide aneffective tool for recovering image history and identifyingtampered images. Although several CE forensic algorithmshave been proposed, their robustness against some processingis still unsatisfactory, such as JPEG compression and anti-forensic attacks. In order to attenuate such deficiency, inthis paper we first present a discriminability analysis of CEforensics in pixel and gray level histogram domains. Then, insuch two domains, two end-to-end methods based on convo-lutional neural networks (P-CNN, H-CNN) are proposed toachieve robust CE forensics against pre-JPEG compressionand anti-forensics attacks. Experimental results show that theproposed methods achieve much better performance than thestate-of-the-art schemes for CE detection in the case of noother operation and comparable performance when pre-JPEGcompression and anti-foresics attacks is used.
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