A New Anti‐forensic Scheme—Hiding the Single JPEG Compression Trace for Digital Image

To prevent image forgeries, a number of forensic techniques for digital image have been developed that can detect an image's origin, trace its processing history, and can also locate the position of tampering. Especially, the statistical footprint left by JPEG compression operation can be a valuable source of information for the forensic analyst, and some image forensic algorithm have been raised based on the image statistics in the DCT domain. Recently, it has been shown that footprints can be removed by adding a suitable anti‐forensic dithering signal to the image in the DCT domain, this results in invalid for some image forensic algorithms. In this paper, a novel anti‐forensic algorithm is proposed, which is capable of concealing the quantization artifacts that left in the single JPEG compressed image. In the scheme, a chaos‐based dither is added to an image's DCT coefficients to remove such artifacts. Effectiveness of both the scheme and the loss of image quality are evaluated through the experiments. The simulation results show that the proposed anti‐forensic scheme can verify the reliability of the JPEG forensic tools.     

Exif信息在数码照片真实性鉴定中的应用

Exif信息检验是数码照片真实性鉴定的一个重要角度,如何充分挖掘和利用Exif信息对于数码照片真实性鉴定具有非常重要的意义.本文在JPEG格式分析基础上,介绍了Exif信息的提取方法,讨论了Exif的缩略图、压缩质量等信息在数码照片真实性鉴定中的应用.     

A study on JPEG steganalytic features: Co-occurrence matrix vs. Markov transition probability matrix

Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features.     

Source Camera Identification for Heavily JPEG Compressed Low Resolution Still Images*

Abstract: In this research, we examined whether fixed pattern noise or more specifically Photo Response Non‐Uniformity (PRNU) can be used to identify the source camera of heavily JPEG compressed digital photographs of resolution 640 × 480 pixels. We extracted PRNU patterns from both reference and questioned images using a two‐dimensional Gaussian filter and compared these patterns by calculating the correlation coefficient between them. Both the closed and open‐set problems were addressed, leading the problems in the closed set to high accuracies for 83% for single images and 100% for around 20 simultaneously identified questioned images. The correct source camera was chosen from a set of 38 cameras of four different types. For the open‐set problem, decision levels were obtained for several numbers of simultaneously identified questioned images. The corresponding false rejection rates were unsatisfactory for single images but improved for simultaneous identification of multiple images.