本文分析了经过伽马校正图像的直方图间隙分布特征和在此基础上进行伽马逆变换后直方图的零值特征，并应用逆变换后直方图的零值特征对篡改图像进行精确参数估计。具体来说，首先根据原始图像直方图零间隙的特征判断图像是否经过伽马校正，然后比较直方图两端零值的数量大小来判断参数所在区间，最后通过逆变换后直方图零值特征对待估计参数进行精确估计。实验结果表明，所提出的方法不仅在参数估计准确率方面优于现有方法，且在变换前为不同质量因子的JPEG图像上同样具有较好鲁棒性。
This paper first analyzes the empty-bin distribution characteristics of the histogram of the gamma-corrected image and the gap feature of the histogram after the inverse gamma transformation on the gramma-corrected image. Subsequently, this gap feature from the inverse transform histogram is applied to estimate the tampered image parameters. Specifically, we first determine whether the image has undergone gamma correction by comparing the number of zero values at both ends of the histogram. Then we estimate the parameters accurately by the inverse-transformed histogram gap feature. Experimental results show that the proposed method outperforms the existing methods in terms of parameter estimation accuracy and demonstrating robustness on JPEG images with different quality factors before the transformation.
[1] Sun J Y, Kim S W, Lee S W, et al. A novel contrast enhancement forensics based on convolutional neural networks [J]. Signal Processing:Image Communication, 2018, 63:149-160.
[2] Zhuang P Y, Li H D, Tan S Q, et al. Image tampering localization using a dense fully convolutional network [J]. IEEE Transactions on Information Forensics and Security, 2021, 16:2986-2999.
[3] Salloum R, Ren Y, Kuo C C J. Image splicing localization using a multi-task fully convolutional network (MFCN) [J]. Journal of Visual Communication and Image Representation, 2018, 51:201-209.
[4] Pomari T, Ruppert G, Rezende E, et al. Image splicing detection through illumination inconsistencies and deep learning [C]//201825th IEEE International Conference on Image Processing (ICIP), 2018:3788-3792.
[5] Bharati A, Moreira D, Flynn P J, et al. Transformation-aware embeddings for image provenance [J]. IEEE Transactions on Information Forensics and Security, 2021, 16:2493-2507.
[6] Wu Y, Abd-Almageed W, Natarajan P. Image copy-move forgery detection via an endto-end deep neural network [C]//2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018:1907-1915.
[7] Zhong J L, Pun C M. An end-to-end dense-inceptionNet for image copy-move forgery detection [J]. IEEE Transactions on Information Forensics and Security, 2020, 15:2134-2146.
[8] Stamm M C, Liu K J R. Forensic detection of image manipulation using statistical intrinsic fingerprints [J]. IEEE Transactions on Information Forensics and Security, 2010, 5(3):492-506.
[9] Cao G, Zhao Y, Ni R R, et al. Contrast enhancement-based forensics in digital images [J]. IEEE Transactions on Information Forensics and Security, 2014, 9(3):515-525.
[10] De Rosa A, Fontani M, Massai M, et al. Second-order statistics analysis to cope with contrast enhancement counter-forensics [J]. IEEE Signal Processing Letters, 2015, 22(8):1132-1136.
[11] Barni M, Costanzo A, Nowroozi E, et al. CNN-based detection of generic contrast adjustment with JPEG post-processing [C]//201825th IEEE International Conference on Image Processing (ICIP), 2018:3803-3807.
[12] Shan W, Yi Y, Huang R, et al. Robust contrast enhancement forensics based on convolutional neural networks [J]. Signal Processing:Image Communication, 2019, 71:138-146.
[13] Yang P P. Dual-domain fusion convolutional neural network for contrast enhancement forensics [J]. Entropy, 2021, 23(10):1318.
[14] Farid H. Blind inverse gamma correction [J]. IEEE Transactions on Image Processing, 2001, 10(10):1428-1433.
[15] Cao G, Zhao Y, Ni R R. Forensic estimation of gamma correction in digital images [C]//2010 IEEE International Conference on Image Processing, 2010:2097-2100.
[16] Vazquez-Corral J, Bertalmío M. Simultaneous blind gamma estimation [J]. IEEE Signal Processing Letters, 2015, 22(9):1316-1320.
[17] Wang P, Liu F, Yang C, et al. Parameter estimation of image gamma transformation based on zero-value histogram bin locations [J]. Signal Processing:Image Communication, 2018, 64:33-45.
[18] Bayar B, Stamm M C. On the robustness of constrained convolutional neural networks to JPEG post-compression for image resampling detection [C]//2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017:2152-2156.
[19] Kumawat C, Pankajakshan V. A robust JPEG compression detector for image forensics [J]. Signal Processing:Image Communication, 2020, 89(11):116008.
[20] Liu R T, Li Z R, Jia J Y. Image partial blur detection and classification [C]//2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008:4587465.
