JPEG二次压缩的分析与检测

为了对JPEG二次压缩后的图像进行有效检测,通过研究JPEG图像经二次压缩后所引起的离散余弦变换(DCT)统计特性的变化及二次压缩前后的量化表q_1,q_2对二次压缩后图像的DCT统计所引起的变化的分析,提出了针对不同q_1,q_2JPEG二次压缩的检测方法.实验结果表明,该检测方法是有效的.     

基于JPEG双量化效应的图像盲取证

JPEG图像的双量化效应为JPEG图像的篡改检测提供了重要线索.根据JPEG图像被局部篡改后,又被保存为JPEG格式时,未被篡改的区域(背景区域)的离散余弦变换(DCT)系数会经历双重JPEG压缩,篡改区域的DCT系数则只经历了1次JPEG压缩.而JPEG图像在经过离散余弦变换后其DCT域的交流(AC)系数的分布符合一个用合适的参数来描述的拉普拉斯分布,在此基础上提出了一种JPEG图像重压缩概率模型来描述重压缩前后DCT系数统计特性的变化,并依据贝叶斯准则,利用后验概率表示出图像篡改中存在的双重压缩效应块和只经历单次压缩块的特征值.然后设定阈值,通过阈值进行分类判断就可以实现对篡改区域的自动检测和提取.实验结果表明,该方法能快速并准确地实现篡改区域的自动检测和提取,并且在第2次压缩因子小于第1次压缩因子时,检测结果相对于利用JPEG块效应不一致的图像篡改盲检测算法和利用JPEG图像量化表的图像篡改盲检测算法有了明显的提高.     

基于块效应分析的JPEG图像处理历史检测

针对JPEG图像及其块效应在空间域中产生的规律性周期模式,提出一种新的方法来分析块效应的周期性,以检测JPEG图像的处理历史。对于品质因子不同的一次压缩JPEG图像和不对齐双压缩JPEG图像来说,它们的块效应峰值能量分布是有差异的。首先构造图像的块效应模型,然后从模型的频谱中提取出峰值窗口,最后从峰值窗口中获得有代表性的特征进行SVM训练。实验结果表明,该方法对于判断一次压缩JPEG图像的压缩强度和检测不对齐双压缩JPEG图像的处理情况是有效且实用的。     

基于数据隐藏的图像检索系统

本文提出一种新的图像检索系统，采用数据隐藏算法把图像的标注信息和特征信息嵌入压缩域图像中，用于快速检索。由于DCT在图像压缩中得到了广泛的应用，本文分析了基于DCT的JPEG图像格式编码流程，实现了一种基于JPEG压缩的数据隐藏算法。实验结果表明，该算法未引起图像质量明显下降，可嵌入较多数据。     

基于混沌的DCT变换域数字水印算法

该算法一方面利用强混沌系统理论中的环形自同构系统对作为水印信息的二值图像进行置乱,另一方面对载体的灰度图像进行分块DCT变换,之后用JPEG标准中的较大压缩率情况下的量化矩阵量化DCT系数。用量化后不为零的DCT系数的统计特征来实现置乱后的水印信息的嵌入。结果表明,该算法较好地解决了鲁棒性（特别对JPEG压缩）和视觉不可见性。     

基于量化表估计的JPEG篡改图像盲检测

传统JPEG篡改图像盲检测算法采用不同质量因子对待测图像进行尝试性再压缩.为了缩短检测时间,减少计算量,提出一种基于量化表估计的JPEG篡改图像盲检测有效算法.首先通过DCT系数直方图估计出篡改图像某一部分量化表,然后通过计算小块DCT系数中分别是对应量化步长整数倍的个数进一步判定该小块是否属于篡改区域.实验结果表明,提出的量化步长估计算法复杂度小,正确率高;检测算法能准确检测出copy-move和合成类图像篡改.此外,改进方法不仅对标准量化表压缩的JPEG篡改图像具有非常好的检测结果,而且对非标准量化表压缩的JPEG篡改图像检测也有效.     

一种基于DCT系数的图像隐藏方法

近年来，随着网络和多媒体技术的快速发展，信息隐藏技术已经成为信息安全领域的一个研究热点。由于JPEG压缩方案中量化是损失信号的主要原因，所以可以在量化后的DCT系数中隐藏信息。文中提出了一种利用对JPEG压缩中的DCT系数进行处理的隐藏图像的方法，将待隐藏图像量化后的DCT系数转化成绝对值小于5的个位数，并将其隐藏在原始图像的量化后的DCT系数中。实验证明该方法能得到不错的隐藏效果，且对原始图像的影响很小。     

一种新的JPEG图像二次压缩检测算法

针对隐写分析中JPEG图像二次压缩的检测问题,提出一种新的压缩检测算法。通过对JPEG一次压缩和二次压缩后的绝对值直方图进行比较,发现直流与交流系数差分绝对值、交流与交流系数差分绝对值在二次压缩后有明显变化。在此基础上提取特征,采用SVM进行分类。实验结果表明,在虚警率小于5%的情况下,该算法检测性能优于Pevny T和Fridrich J提出的算法(IEEE Trans. on Information Forensics and Security, 2007, No.2)。     

一种抗JPEG压缩的数字水印算法

该文提出了一种新的有效的抗JPEG压缩的水印算法,对嵌入水印的图像做JPEG压缩,提取水印仍能保证很好的稳健性,并能抵抗一些常见的攻击。通过对图像做仿JPEG流程的处理,在DCT变换的直流系数上嵌入水印,对于各种常用的JPEG压缩量化因子,可以得到良好的水印嵌入效果。     

基于差值抖动调制的鲁棒数字水印

针对数字图像版权保护问题,提出了一种新的鲁棒水印方案.该方案首先对水印进行扩频调制,然后随机选取图像8×8分块DCT变换后的中低频系数对来嵌入水印信息,为了使水印能够抵抗JPEG压缩,提出了基于差值抖动调制的嵌入方法.最后对影响水印鲁棒性和不可见性的主要因素进行了分析.实验结果表明,该方案不仅能够抵抗JPEG压缩和剪切攻击,而且能够抵抗一定程度的低通滤波攻击.     

Double JPEG compression detection based on block statistics

In this paper, a novel method is proposed to detect the aligned double JPEG compression with different quantization matrix. The proposed method is based on the theory that the correlation among adjacent coefficients of frequency spectrum in DCT blocks is enhanced after DCT transformation, and the correlation among same locations in adjacent DCT blocks is strong. Classification features are divided into two types, the intra-block frequency domain features and the inter-block frequency domain features. The intra-block frequency domain features are used to catch the strong correlation among adjacent coefficients in DCT blocks, and the inter-block frequency domain features are used to catch the correlation among same locations of adjacent DCT blocks. Then the intra-block frequency domain features with inter-block frequency domain features are combined as the classification features. Finally, the classification features are used to train classifiers to detect double JPEG compression. The results of extensive experiments demonstrate the effectiveness of the proposed method.     

Hiding traces of double compression in JPEG images based on Tabu Search

With the development of digital forensic techniques, the image disguise (also called image tampering or image manipulation) is challenged greatly. When the tampered JPEG image is saved, it was re-compressed. This progress causes radical change in the histogram of the discrete cosine transformation (DCT) coefficients. The amounts of some coefficient values decrease greatly or even disappear. It is easily to detect this pattern by statistics methods. To remove the fragility of image disguise, we need to revise the abnormal distribution of DCT coefficients. But random change in the coefficient values degrades the image quality. So we established an image disguising evaluation model aims at the JPEG recompression detection which can be solved by Tabu Search algorithm. Under the restraint of the evaluation model, the traces of double compression are removed, and the image quality is well preserved.

     

Detection of JPEG double compression and identification of smartphone image source and post-capture manipulation

Digital multimedia forensics is an emerging field that has important applications in law enforcement and protection of public safety and national security. In digital imaging, JPEG is the most popular lossy compression standard and JPEG images are ubiquitous. Today’s digital techniques make it easy to tamper JPEG images without leaving any visible clues. Furthermore, most image tampering involves JPEG double compression, it heightens the need for accurate analysis of JPEG double compression in image forensics. In this paper, to improve the detection of JPEG double compression, we transplant the neighboring joint density features, which were designed for JPEG steganalysis, and merge the joint density features with marginal density features in DCT domain as the detector for learning classifiers. Experimental results indicate that the proposed method improves the detection performance. We also study the relationship among compression factor, image complexity, and detection accuracy, which has not been comprehensively analyzed before. The results show that a complete evaluation of the detection performance of different algorithms should necessarily include image complexity as well as the double compression quality factor. In addition to JPEG double compression, the identification of image capture source is an interesting topic in image forensics. Mobile handsets are widely used for spontaneous photo capture because they are typically carried by their users at all times. In the imaging device market, smartphone adoption is currently exploding and megapixel smartphones pose a threat to the traditional digital cameras. While smartphone images are widely disseminated, the manipulation of images is also easily performed with various photo editing tools. Accordingly, the authentication of smartphone images and the identification of post-capture manipulation are of significant interest in digital forensics. Following the success of our previous work in JPEG double compression detection, we conducted a study to identify smartphone source and post-capture manipulation by utilizing marginal density and neighboring joint density features together. Experimental results show that our method is highly promising for identifying both smartphone source and manipulations. Finally, our study also indicates that applying unsupervised clustering and supervised classification together leads to improvement in identifying smartphone sources and manipulations and thus provides a means to address the complexity issue of the intentional post-capture manipulation on smartphone images.     

No-reference image quality assessment based on localized discrete cosine transform for JPEG compressed images

In this paper, we propose a new no-reference image quality assessment for JPEG compressed images. In contrast to the most existing approaches, the proposed method considers the compression processes for assessing the blocking effects in the JPEG compressed images. These images have blocking artifacts in high compression ratio. The quantization of the discrete cosine transform (DCT) coefficients is the main issue in JPEG algorithm to trade-off between image quality and compression ratio. When the compression ratio increases, DCT coefficients will be further decreased via quantization. The coarse quantization causes blocking effect in the compressed image. We propose to use the DCT coefficient values to score image quality in terms of blocking artifacts. An image may have uniform and non-uniform blocks, which are respectively associated with the low and high frequency information. Once an image is compressed using JPEG, inherent non-uniform blocks may become uniform due to quantization, whilst inherent uniform blocks stay uniform. In the proposed method for assessing the quality of an image, firstly, inherent non-uniform blocks are distinguished from inherent uniform blocks by using the sharpness map. If the DCT coefficients of the inherent non-uniform blocks are not significant, it indicates that the original block was quantized. Hence, the DCT coefficients of the inherent non-uniform blocks are used to assess the image quality. Experimental results on various image databases represent that the proposed blockiness metric is well correlated with the subjective metric and outperforms the existing metrics.

     

基于JPEG的固定背景视频压缩算法研究

为了对固定背景视频进行压缩并获得较高的压缩比,在JPEG静止图像压缩标准的基础上提出了一种新的应用于固定背景视频压缩的算法.对第一帧图像进行JPEG格式的压缩并保存量化后的离散余弦变换系数,对第一帧后的每一帧图像,在进行离散余弦变换和量化后,先同存储器内的第一帧图像的离散余弦变换系数进行异或运算再进行熵编码.通过使用该算法和H.264视频压缩标准对同一段固定背景视频进行压缩并比较压缩后的数据量,表明了该算法具有较高的压缩比.     

Improved anti-forensics of JPEG compression

The comblike histogram of DCT coefficients on each subband and the blocking artifacts among adjacent blocks are the two main fingerprints of the image that was once compressed by JPEG. Stamm and Liu proposed an anti-forensics method for removing these fingerprints by dithering the DCT coefficients and adding noise into the pixels. However, some defects emerge inside the anti-forensically processed images. First, the noise distributions are abnormal in the resulting images; and second, the quality of the processed image is poor compared with the original image. To fill these gaps, this paper proposes an improved anti-forensics method for JPEG compression. After analyzing the noise distribution, we propose a denoising algorithm to remove the grainy noise caused by image dithering, and a deblocking algorithm to combat Fan and Queiroz's forensics method against blocking artifacts. With the proposed anti-forensics method, fingerprints of the comblike histograms and the blocking artifacts are removed, noise distribution abnormality is avoided, and the quality of the processed image is improved.     

Forensics for partially double compressed doctored JPEG images

Digital image forensics is required to investigate unethical use of doctored images by recovering the historic information of an image. Most of the cameras compress the image using JPEG standard. When this image is decompressed and recompressed with different quantization matrix, it becomes double compressed. Although in certain cases, e.g. after a cropping attack, the image can be recompressed with the same quantization matrix too. This JPEG double compression becomes an integral part of forgery creation. The detection and analysis of double compression in an image help the investigator to find the authenticity of an image. In this paper, a two-stage technique is proposed to estimate the first quantization matrix or steps from the partial double compressed JPEG images. In the first stage of the proposed approach, the detection of the double compressed region through JPEG ghost technique is extended to the automatic isolation of the doubly compressed part from an image. The second stage analyzes the doubly compressed part to estimate the first quantization matrix or steps. In the latter stage, an optimized filtering scheme is also proposed to cope with the effects of the error. The results of proposed scheme are evaluated by considering partial double compressed images based on the two different datasets. The partial double compressed datasets have not been considered in the previous state-of-the-art approaches. The first stage of the proposed scheme provides an average percentage accuracy of 95.45%. The second stage provides an error less than 1.5% for the first 10 DCT coefficients, hence, outperforming the existing techniques. The experimental results consider the partial double compressed images in which the recompression is done with different quantization matrix.     

一种改进的JPEG算法研究

图像压缩技术是视频信息存储和传输的关键技术之一,本文首先分析了标准JPEG算法的过程和不足,理论上讨论了切匹雪夫(HEBYCHEV)算法较DCT变换的优越性,并且引入自适应量化算法对标准JPEG的量化表加以重构,通过微机仿真表明在相同压缩比的条件下较标准JPEG的恢复图像信噪比有所提高,同时恢复图像的质量也有改进,这种方法有一定的价值和实用性。     

基于C语言的图像压缩算法

该文借鉴静态图像压缩标准JPEG的理论研究成果,将其与DCT快速变换相结合,采用霍夫曼编码方法,用C语言编程实现灰度图像的压缩。最后,计算了基于DCT快速变换的图像压缩算法的压缩比。同时,分析了DCT快速变换后的数据,验证了该算法用于图像压缩的合理性。