一种基于HVS的图像易碎水印

作为多媒体认证和篡改检测的一种新技术,易碎水印正得到越来越多的关注.基于小波变换,结合量化调制过程,本文提出了一个易碎水印算法.该算法具有以下特点:(1)构造水印金字塔,便于实现水印的多分辨率检测;(2)结合视觉特性量化调制,尽可能减小视觉失真;(3)结合图像融合技术的多分辨率检测,使检测结果更准确;(4)抵抗一定程度的JPEG有损压缩.为了实现篡改的检测,本文给出了一个攻击判别方案,以区分恶意攻击和偶然攻击.实验表明,嵌入的水印在脆弱性和鲁棒性上达到了较好的统一,能够抵抗一定程度的压缩而不影响篡改检测的精确性.     

JPEG图像重压缩检测综述

JPEG（Joint Photographic Experts Group,联合图像专家小组）是目前互联网上运用最为广泛的图像格式。已有的案例表明,许多篡改操作都发生在JPEG图像上,其操作基本流程是首先对JPEG文件进行解压,在空域进行篡改,篡改完成后再将篡改后的图片压缩保存为JPEG格式,这样篡改后的图片就可能会被两次甚至多次JPEG压缩。因此,JPEG图像的重压缩检测可以作为判断图像是否经过篡改的重要依据,对JPEG图像进行分析和取证具有非常重要的意义。本文主要从JPEG重压缩过程中量化表保持不变和量化表不一致这两个方面,对近年来JPEG重压缩检测领域的文献进行了一个回顾,介绍了该领域一些代表性的方法。最后我们还分析了JPEG重压缩领域存在的问题,并对未来的发展方向进行了展望。      

基于JPEG压缩不变量和数字水印的图像认证方法

提出一种抗JPEG压缩的图像认证算法。根据图像不同块中位置相同的DCT系数之间的大小关系在JPEG压缩之后几乎没有发生变化这一性质,对图像进行分析生成特征编码,并将特征编码加密后以水印方式嵌入图像。认证时只需将待测图像的特征编码与从中抽取出的解密水印进行比较,利用篡改矩阵鉴别是否有内容被篡改,并给出具体的篡改位置。实验结果表明,该算法有很好的抗JPEG压缩性能,并可有效地检测出恶意篡改及其发生的位置。     

版权标识与篡改定位的数字图像水印方案

针对JPEG图像提出了用于版权标识和篡改定位功能的图像水印算法.水印嵌入和提取算法基于JPEG图像解压缩后的像素矩阵进行.水印算法结合了JPEG压缩核心技术DCT变换系数进行版权标识水印和篡改定位水印的嵌入和提取处理过程.水印算法性能测试对象为银行票据图像,实验结果包括给出了水印算法的图像体积增幅比、PSNR值以及水印误比特率指标结果.实验结果表明,算法在版权标识和篡改定位方面均具有较好的实用性.     

基于双混沌系统的抗JPEG压缩的图像认证水印算法

提出了一种新的兼具水印高安全性和高定位精度的抗JPEG压缩的图像认证水印算法.根据JPEG图像压缩过程中DCT系数的不变特性,将预先量化的DCT低频系数和水印密钥合成为混沌系统的初值.再将图像块的编号映射为混沌系统的迭代次数,经过logistic混沌迭代生成图像块水印.在水印嵌入过程中,利用另一混沌系统猫映射构造循环结构,建立图像块之间一一对应的循环关系,并将块水印嵌入到对应图像块的DCT中频系数中.实验结果表明,该算法对JPEG有损压缩具有良好的鲁棒性,同时可对图像内容的恶意篡改进行精确的检测与定位.     

基于HVS的小波域图像易损水印

基于小波变换,结合量化调制过程,论文提出了一种易损水印算法。该算法具有构造分层水印,便于实现水印的多分辨率嵌入;结合视觉特性量化调制,尽可能减小视觉失真;结合图像融合技术的多分辨率检测,使检测结果更准确;以技抵抗一定程度的JPEG有损压缩等特点。实验表明,嵌入的水印在易损性和鲁棒性上达到了较好的统一,能够抵抗一定程度的压缩而不影响篡改检测的精确性。     

一种基于半脆弱水印的图像内容认证算法

本文提出了一种新的基于半脆弱水印的图像认证算法。利用JPEG压缩算法的不变属性,将原始图像先经过预定质量因子的压缩,并通过一种无损数据隐藏算法将基于图像内容特征的水印信息嵌入。认证过程中,将提取出的水印信息与计算出的图像特征信息比特的匹配百分比与设定的阈值比较,决定是否通过认证。实验结果表明:算法对高于预定质量因子的JPEG压缩具有良好的鲁棒性,拒绝恶意篡改并精确定位篡改位置,同时有效地挫败共谋攻击。     

能区分图像或水印篡改的脆弱水印方案

针对现有脆弱型水印方案不能区分是图像内容还是水印被篡改的问题,提出一种能区分图像或水印篡改的脆弱水印方案.该方案用原始图像高7位的小波低频系数非均匀量化后生成的低频压缩图像作为水印,并用混沌系统对水印进行置乱加密,将安全性得到增强的水印直接嵌入到图像的LSB位;认证时通过差值图像定位图像内容被篡改的位置并指出图像中的水印是否被篡改.理论分析和仿真实验表明:该算法不但能精确定位图像内容被篡改的位置,而且能区分是图像内容被篡改、水印被篡改还是两者同时被篡改.     

块级篡改定位的JPEG图像脆弱水印

 JPEG是一种常见的图像格式,在JPEG图像中进行准确的篡改定位具有重要意义. 本文提出一种新的JPEG图像脆弱水印方案,将每个小块主要内容的Hash比特重新分组,并将每组的模2和作为水印信息. 也就是说每一个小块都对应多个水印比特,每个水印比特也对应多个小块. 载体图像的每个小块中仅嵌入于1比特水印,保证了良好的隐蔽性. 认证时依据图像内容与水印比特的整体匹配情况估计篡改率,再根据每个小块对应的水印信息的被破坏程度判别该小块是否曾被篡改. 理论分析和实验结果标明该方法可以在篡改区域小于1%的情况下准确地找到所有篡改小块.     

基于BJND的立体图像篡改定位及恢复水印方法

针对立体图像内容的真实性认证和完整性保护等 问题,提出一种基于双目恰可觉察失 真(BJND)的立体图像 篡改定位及恢复水印方法。首先,利用奇异值的稳定特性,设计左右图像的定位水印;然后 ,根据BJND强度将左右图像 的定位水印嵌入到左图像中;最后,利用离散余弦变换(DCT)和JPEG量化压缩 生成恢复信息,并将右图像和左图像遮挡暴露区 域的恢复信息分别嵌入到右图像和左图像中。实验结果表明,本文方法所构造的水印对JPEG 压缩、椒盐噪声、高斯白噪声等偶然攻击比较鲁棒,而对剪切、拼接、旋转等恶意攻击较为 脆弱,同时对左右图像的恶意篡改区域检测率大于98%。由于充分利用了左右图像的匹配特 性,恢复的被篡改立体图像其左右图像PSNR达36.02dB ～41.29dB。     

A novel deep learning framework for double JPEG compression detection of small size blocks

Double JPEG compression detection plays a vital role in multimedia forensics, to find out whether a JPEG image is authentic or manipulated. However, it still remains to be a challenging task in the case when the quality factor of the first compression is much higher than that of the second compression, as well as in the case when the targeted image blocks are quite small. In this work, we present a novel end-to-end deep learning framework taking raw DCT coefficients as input to distinguish between single and double compressed images, which performs superior in the above two cases. Our proposed framework can be divided into two stages. In the first stage, we adopt an auxiliary DCT layer with sixty-four 8 × 8 DCT kernels. Using a specific layer to extract DCT coefficients instead of extracting them directly from JPEG bitstream allows our proposed framework to work even if the double compressed images are stored in spatial domain, e.g. in PGM, TIFF or other bitmap formats. The second stage is a deep neural network with multiple convolutional blocks to extract more effective features. We have conducted extensive experiments on three different image datasets. The experimental results demonstrate the superiority of our framework when compared with other state-of-the-art double JPEG compression detection methods either hand-crafted or learned using deep networks in the literature, especially in the two cases mentioned above. Furthermore, our proposed framework can detect triple and even multiple JPEG compressed images, which is scarce in the literature as far as we know.     

DCT-based video downscaling transcoder using split and merge technique.

For a conventional downscaling video transcoder, a video server has firstly to decompress the video, perform downscaling operations in the pixel domain, and then recompress it. This is computationally intensive. However, it is difficult to perform video downscaling in the discrete cosine transform (DCT)- domain since the prediction errors of each frame are computed from its immediate past higher resolution frames. Recently, a fast algorithm for DCT domain image downsampling has been proposed to obtain the downsampled version of DCT coefficients with low computational complexity. However, there is a mismatch between the downsampled version of DCT coefficients and the resampled motion vectors. In other words, significant quality degradation is introduced when the derivation of the original motion vectors and the resampled motion vector is large. In this paper, we propose a new architecture to obtain resampled DCT coefficients in the DCT domain by using the split and merge technique. Using our proposed video transcoder architecture, a macroblock is splitted into two regions: dominant region and the boundary region. The dominant region of the macroblock can be transcoded in the DCT domain with low computational complexity and re-encoding error can be avoided. By transcoding the boundary region adaptively, low computational complexity can also be achieved. More importantly, the re-encoding error introduced in the boundary region can be controlled more dynamically. Experimental results show that our proposed video downscaling transcoder can lead to significant computational savings as well as videos with high quality as compared with the conventional approach. The proposed video transcoder is useful for video servers that provide quality service in real-time for heterogeneous clients.     

基于能量估计和视觉模型的数字水印算法

本文将数字水印能量估计方法拓展到分块离散余弦变换 (DCT)域,并且提出了一种基于分块DCT的数字水印算法,将能量估计方法应用于该算法中。该算法利用了人眼视觉系统 (HVS)模型,将水印信息嵌入到DCT系数的低、中频域,试图达到不可感知性和鲁棒性的最佳折衷,进而提出了一种灵活的重复嵌入策略及相应的检测算法。实验结果表明,该算法在较大的信息嵌入量情况下仍然具有较好的不可感知性和对多种攻击的鲁棒性。     

JPEG图像中的安全密写方案

JPEG是一种常见的图像格式,在JPEG图像中进行密写有着重要的实际意义。但以往的密写技术往往会改变载体图像的DCT系数直方图或增大分块效应,分析者可以据此察觉秘密信息的存在。为了提高密写安全性,该文提出一种新的JPEG密写方案。该方案在将秘密信息嵌入非0的非直流DCT系数时,既不改变原始图像的DCT系数直方图,也不增加载体图像的分块效应,因此可以抵抗基于直方图和基于分块特性的密写分析。实验表明,当载体图像的质量因子大于35时,秘密信息的安全性可以得到有效保证。     

基于FPGA的二维DCT变换的实现

二维离散余弦变换(DCT)在图像处理和视频编码中起重要的作用。以MPEG 2全I帧编码为背景,在现场可编程门阵列(FPGA)上实现8×8像素的二维DCT变换。算法首先把8×8像素的二维DCT变换化简成8次一维DCT变换加上适当的蝶形运算和顺序重排操作。试验表明,方案可以只用一个一维DCT模块实现输入采样率为74.25MHz的二维DCT变换。     

一种基于FPGA的二维DCT和IDCT的新算法

提出了一种新的二维DCT和IDCT的FPGA实现结构,采用行列快速算法将二维算法分解为两个一维算法实现,其中每个一维算法采用并行的流水线结构,每一个时钟处理8个数据,大大提高电路的数据吞吐率和运算速度。通过Modelsim仿真工具对该设计进行仿真,证明该算法的功能的正确性,进行一次8*8的分块二维DCT变换仅仅需要16个时钟,满足图像以及视频实时性的要求。     

基于DCT变换的快速分形编码方法

本文提出了一种基于DCT变换加速相似块匹配的分形编码方法.通过图像块与相似块在DCT变换域少数低频系数的比较,来确定匹配的相似块.并根据DCT变换的性质,方便的确定对比度因子和亮度偏移量.并采用将图像块与相似块的DCT变换系数预制成查找表的方法减少重复运算.结果表明,编码时间大幅度减少,而PSNR只是略有下降.     

Improvements of Transform Coding Algorithm for Motion-Compensated Interframe Prediction Errors-DCT/SQ Coding

A new hybrid coding method for transmitting videoconferencing images at a bit rate 384 kbits/s is proposed. Considering the characteristics of motion-compensated interframe prediction errors for typical videoconferencing scenes, a filter is introduced to separate pulsive components on which conventional discrete cosine transform (DCT) coding method does not work well. These separated pulsive components are coded by using scalar quantization (SQ). The remainder are DCT coded. For DCT coefficients, an adaptive coding method based on the classification of DCT coefficients is applied in order to improve the coding performance. Since the proposed method employs both DCT coding and SQ of prediction errors, it is named "DCT/SQ coding method." Experimental results show that the DCT/SQ coding method is effective to reduce so-called mosquito effects, and thus it can improve the quality of decoded images.     

Coarse-to-fine outlier correction with applications in structure from motion

This paper presents a robust method for outlier detection and correction in structure from motion. Aiming at handling outliers together with missing data, the Discrete Cosine Transform (DCT) based Column Space Fitting (CSF) algorithm is extended and improved. The use of the DCT basis allows for a coarse-to-fine optimization strategy that reconstructs 3D scene geometry and camera motion by increasing the number of DCT basis vectors. With a certain DCT basis, an interior point based L₁-norm solver is used to successively estimate 3D scene structure. In addition, the fidelity of the estimated camera motion matrix is first integrated into an extension of Huber M-estimator to find outliers and to robustly estimate the update magnitude for each outlier. This fidelity can be measured by the effects of camera motion matrix on re-projection errors. Because the Huber M-estimator is only applicable to vector, we extend it into the matrix form. With the increase in the number of DCT basis vectors, outliers are corrected in a coarse-to-fine manner. Experiments on both synthetic and real image sequences confirm the effectiveness of the proposed method.