Passive detection of copy-paste forgery between JPEG images

A blind digital image forensic method for detecting copy-paste forgery between JPEG images was proposed.Two copy-paste tampering scenarios were introduced at first:the tampered image was saved in an uncompressed format or in a JPEG compressed format.Then the proposed detection method was analyzed and simulated for all the cases of the two tampering scenarios.The tampered region is detected by computing the averaged sum of absolute difference(ASAD) images between the examined image and a resaved JPEG compressed image at different quality factors.The experimental results show the advantages of the proposed method:capability of detecting small and/or multiple tampered regions,simple computation,and hence fast speed in processing.

Image Tampering Detection Using No-Reference Image Quality Metrics

In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information of the original image is a challenging problem since unknown diverse manipulations may have different characteristics and so do various formats of images.Our principle is that image processing,no matter how complex,may affect image quality,so image quality metrics can be used to distinguish tampered images.In particular,based on the alteration of image quality in modified blocks,the proposed method can locate the tampered areas.Referring to four types of effective no-reference image quality metrics,we obtain 13 features to present an image.The experimental results show that the proposed method is very promising on detecting image tampering and locating the locally tampered areas especially in realistic scenarios.

Image Tamper Detection and Multi-Scale Self-Recovery Using Reference Embedding with Multi-Rate Data Protection

This paper proposes a multi-scale self-recovery(MSSR)approach to protect images against content forgery.The main idea is to provide more resistance against image tampering while enabling the recovery process in a multi-scale quality manner.In the proposed approach,the reference data composed of several parts and each part is protected by a channel coding rate according to its importance.The first part,which is used to reconstruct a rough approximation of the original image,is highly protected in order to resist against higher tampering rates.Other parts are protected with lower rates according to their importance leading to lower tolerable tampering rate(TTR),but the higher quality of the recovered images.The proposed MSSR approach is an efficient solution for the main disadvantage of the current methods,which either recover a tampered image in low tampering rates or fails when tampering rate is above the TTR value.The simulation results on 10000 test images represent the efficiency of the multi-scale self-recovery feature of the proposed approach in comparison with the existing methods.

An Overview of Image Tamper Detection

With the popularization of high-performance electronic imaging equipment and the wide application of digital image editing software,the threshold of digital image editing becomes lower and lower.Thismakes it easy to trick the human visual system with professionally altered images.These tampered images have brought serious threats to many fields,including personal privacy,news communication,judicial evidence collection,information security and so on.Therefore,the security and reliability of digital information has been increasingly concerned by the international community.In this paper,digital image tamper detection methods are classified according to the clues that they rely on,detection methods based on image content and detection methods based on double JPEG compression traces.This paper analyzes and discusses the important algorithms in several classification methods,and summarizes the problems existing in various methods.Finally,this paper predicts the future development trend of tamper detection.

A Survey of Blind Forensics Techniques for JPEG Image Tampering

Blind forensics of JPEG image tampering as a kind of digital image blind forensics technology is gradually becoming a new research hotspot in the field of image security. Firstly, the main achievements of domestic and foreign scholars in the blind forensic technology of JPEG image tampering were briefly described. Then, according to the different methods of tampering and detection, the current detection was divided into two types: double JPEG compression detection and block effect inconsistency detection. This paper summarized the existing methods of JPEG image blind forensics detection, and analyzed the two methods. Finally, the existing problems and future research trends were analyzed and prospected to provide further theoretical support for the research of JPEG image blind forensics technology.

An Active Image Forgery Detection Approach Based on Edge Detection

Recently, digital images have become the most used data, thanks tohigh internet speed and high resolution, cheap and easily accessible digitalcameras. We generate, transmit and store millions of images every second.Most of these images are insignificant images containing only personal information.However, in many fields such as banking, finance, public institutions,and educational institutions, the images of many valuable objects like IDcards, photographs, credit cards, and transaction receipts are stored andtransmitted to the digital environment. These images are very significantand must be secured. A valuable image can be maliciously modified by anattacker. The modification of an image is sometimes imperceptible even by theperson who stored the image. In this paper, an active image forgery detectionmethod that encodes and decodes image edge information is proposed. Theproposed method is implemented by designing an interface and applied on atest image which is frequently used in the literature. Various tampering attacksare simulated to test the fidelity of the method. The method not only notifieswhether the image is forged or not but also marks the tampered region ofthe image. Also, the proposed method successfully detected tampered regionsafter geometric attacks, even on self-copy attacks. Also, it didn’t fail on JPEGcompression.

Deep Learning Based Image Forgery Detection Methods

Increasingly advanced image processing technology has made digital image editing easier and easier.With image processing software at one’s fingertips,one can easily alter the content of an image,and the altered image is so realistic that it is illegible to the naked eye.These tampered images have posed a serious threat to personal privacy,social order,and national security.Therefore,detecting and locating tampered areas in images has important practical significance,and has become an important research topic in the field of multimedia information security.In recent years,deep learning technology has been widely used in image tampering localization,and the achieved performance has significantly surpassed traditional tampering forensics methods.This paper mainly sorts out the relevant knowledge and latest methods in the field of image tampering detection based on deep learning.According to the two types of tampering detection based on deep learning,the detection tasks of the method are detailed separately,and the problems and future prospects in this field are discussed.It is quite different from the existing work:(1)This paper mainly focuses on the problem of image tampering detection,so it does not elaborate on various forensic methods.(2)This paper focuses on the detectionmethod of image tampering based on deep learning.(3)This paper is driven by the needs of tampering targets,so it pays more attention to sorting out methods for different tampering detection tasks.

Comparative Evaluation of Semi Fragile Watermarking Algorithms for Image Authentication

Technology has no limits today;we have lots of software available in the market by which we can alter any image. People usually copies image from the internet and after some changes they claim that these are their own properties. Insuring digital image integrity has therefore become a major issue. Over the past few years, watermarking has emerged as the leading candidate to solve problems of ownership and content authentications for digital multimedia documents. To protect authenticity of images semi fragile watermarking is very concerned by researchers because of its important function in multimedia content authentication. The aim of this paper is to present a survey and a comparison of emerging techniques for image authentication using semifragile watermarking. In present paper comprehensive overview of insertion and extraction methods used in different semi fragile water marking algorithm are studied using image parameters, potential application, different algorithms are described and focus is on their comparison according to the properties cited above and future directions for developing a better image authentication algorithm are suggested.

Extraction of color-intensity feature towards image authentication

A color-intensity feature extraction method is proposed aimed at supplementing conventional image hashing algorithms that only consider intensity of the image. An image is mapped to a set of blocks represented by their dominant colors and average intensities. The dominant color is defined by hue and saturation with the hue value adjusted to make the principal colors more uniformly distributed. The average intensity is extracted from the Y component in the YCbCr space. By quantizing the color and intensity components, a feature vector is formed in a cylindrical coordinate system for each image block, which may be used to generate an intermediate hash. Euclidean distance is modified and a similarity metric introduced to measure the degree of similarity between images in terms of the color-intensity features. This is used to validate effectiveness of the proposed feature vector. Experiments show that the color-intensity feature is robust to normal image processing while sensitive to malicious alteration, in particular, color modification.

A New Fragile Watermark for Image

Fragile watermarking is a method to verify the integrity and authenticity of multimedia data. A new fragile watermark for image was proposed, which can be used in image verification applications. The paper first described the above two techniques, some of which will be used in the method. Then it described the embedding and authentication process and also analyzed the method to show how it can survive some attacks. The experimental results show that the proposed method doesn’t need the watermark or original image on authentication side. It provides more security against attack, and can localize where the tempering has occurred.

边缘异常识别下视频图像篡改细节检测

视频图像不同栅格位置或不同压缩区域被合成为篡改图像时会出现特征块效应的差异,改变原视频的关键信息。为了准确识别图像中被篡改的像素点,提出基于边缘异常识别的视频图像篡改检测方法。通过离散余弦变换,将能量全部集中到图像的低频系数内,描述出视频的边缘等细节。利用能量比与频域熵间关系得出图像中能量的可疑度,结合预测掩膜概率图划分出发生篡改的位置区域。利用Sobel边缘检测边缘点,量化边缘点特征判断出边缘是否异常,当出现异常对其跟踪直至目标消失,检测出视频图像中的篡改位置区域。实验结果表明,所提方法能够精准检测出视频图像被篡改位置,且耗时低于1ms,应用优势显著。

一种基于保守超混沌系统的脆弱水印算法

提出了一种新颖的基于混沌系统的数字图像脆弱水印算法。所提出的方案引入了一个高维保守超混沌系统与数字图像关联生成混沌序列,通过转置卷积重构后得到水印信息,将其嵌入到图像像素的最低有效位(Least Significant Bit,LSB)。相比耗散混沌,保守超混沌系统因其不具有混沌吸引子的性质和超混沌特性,可以在符合人眼视觉的掩蔽性的同时,增强安全性能。实验结果表明,该方法安全可靠,能够在各种攻击下获得较高的篡改检测和定位精度。

基于SVD的分块半脆弱水印医学图像算法研究

为确保医学图像的完整性与安全性,提出一种基于奇异值分解的分块半脆弱水印医学图像算法,具备定位图像篡改区域以及图像自恢复两种功能。该算法将图像按4×4大小分块,并将水印嵌入位置分为块认证位和自恢复位两种,其中块认证位用于认证每个块,确保该块被篡改时能被识别出来;自恢复位则用于实现图像被篡改后的自恢复。将分块通过奇异值分解生成块认证位,同时将块划分为2×2的块,计算它们的5MSB平均值以生成自恢复位。仿真实验结果表明,所提算法的PSNR指标均在50 dB以上,大部分篡改攻击的Rfa和Rfd接近于0,且自恢复图像PSNR指标在30 dB以上。所提算法具有较好的透明性,能准确检测大部分篡改攻击,且自恢复图像质量良好。

用于篡改图像检测和定位的双通道渐进式特征过滤网络

针对现有基于深度学习的篡改图像检测网络通常存在检测精度不高、算法可迁移性弱等问题,提出一种双通道渐进式特征过滤网络。利用两个通道并行提取图像的双域特征,一个通道提取图像空间域的浅层和深层特征,另一个通道提取图像噪声域的特征分布;同时,使用渐进式细微特征筛选机制过滤冗余特征,逐步定位篡改区域;为了更准确地提取篡改掩码,提出一个双输入细微特征提取模块,结合空间域和噪声域的细微特征,生成更准确的篡改掩码;在解码过程中,通过融合不同尺度的过滤特征和网络的上下文信息,提高网络对篡改区域的定位能力。实验结果表明,在检测和定位方面,与现有先进的篡改检测网络ObjectFormer、MVSS-Net(Multi-View multi-Scale Supervision Network)和PSCC-Net(Progressive Spatio-Channel Correlation Network)相比,所提网络的F1分数在CASIA V2.0数据集上分别提高10.4、5.9和12.9个百分点;面对高斯低通滤波、高斯噪声和JPEG压缩攻击时,相较于ManTra-Net(Manipulation Tracing Network)、SPAN(Spatial Pyramid Attention Network),所提网络的曲线下面积(AUC)分别至少提高了10.0、5.4个百分点。验证了所提网络可以有效解决篡改检测算法存在的检测精度不高、迁移性差等问题。

结合自注意力与卷积的真实场景图像篡改定位

图像是移动互联网时代传播信息的重要载体,恶意图像篡改是潜在的网络安全威胁之一。与自然场景中在物体尺度上的图像篡改不同,真实场景中的图像篡改存在于伪造的资质证书、文案、屏幕截图等,这些篡改图像通常会经过精心的手工篡改干预,因此其篡改特征与自然场景篡改特征存在差异,更具有多样性,对其篡改区域的定位更具有挑战性。针对该场景复杂且多样的篡改特征,丰富的关系信息是重要的,文中通过卷积神经网络进行自适应特征提取,并利用逆向连接的全自注意力模块进行多阶段特征关注,最后融合多阶段注意力关注结果进行篡改区域定位。所提方法在真实场景图像篡改定位任务中取得了优于对比方法的性能,其中F 1指标比主流方法MVSS-Net高出约8.98%,AUC指标高出约3.58%。此外,所提方法在自然场景图像篡改定位任务中也达到了主流方法的性能,并提供了自然场景篡改特征与真实场景篡改特征存在差异的佐证。在两种场景中的实验结果表明,所提方法能够有效地定位出篡改图像的篡改区域,且在复杂的真实场景中的定位效果更显著。

基于特征融合与局部异常的图像篡改检测

数字图像处理技术的不断发展使得图像篡改检测任务越来越具有挑战性。图像篡改检测方法往往需对整个图像进行无差异检测,极大程度地增加了模型学习的复杂度;此外不同的图像大小和采样、卷积、池化等操作也会丢失部分图像的空间信息和篡改特征。为尽量减少特征信息丢失,文中所提的模型通过对上采样和下采样的对应特征进行融合以及将噪声流和约束卷积流与模型的卷积特征进行融合,保存图像的低级语义信息;其次,基于分割模型构建了一个局部异常区域检测机制,重点关注图像异常区域,减少模型复杂度;最后利用注意力增强对篡改特征的关注,解决语义信息不明确的问题。提出的篡改检测方法在三个基准数据集上取得了最优性能,且与最新方法进行了对比试验,在CASIA数据集上F1值提升了15.9%,NIST2016数据集提升了4.5%,COVERAGE数据集提升了5.7%。

基于IPVO的高效图像可逆认证方法

提出了一种基于小波域预测误差直方图平移的图像可逆认证方法。首先,对待认证图像进行一次哈尔小波变换;其次,将变换得到的低频子带和高频子带分别分块,并根据块内的系数值和块的位置生成认证码;然后,使用改进的像素值排序(IPVO)方法将认证码嵌入到分块中,通过哈尔小波逆变换得到包含认证信息的图像;在认证阶段,通过对比分块提取认证码和生成认证码来实现篡改检测功能,并可无损地恢复原始图像。实验结果表明,该方法提供了更准确的检测结果,并确保了一定水平的图像质量。

采用独立可逆认证的图像篡改检测方法

针对现有的图像篡改检测方法存在篡改定位不精确以及未受篡改区域不能有效进行无损恢复的问题,提出采用独立可逆认证的图像篡改检测方法。首先,采用图像分块的独立可逆认证方法,结合可逆信息隐藏算法的嵌入原理,将现有认证方法中的认证单元由常规的4×4图像子块缩小为4×3图像子块,解决了认证单元偏大的问题,提高了篡改检测的定位精度;然后,根据子块特点,自适应地选取改进的像素值排序、局部直方图平移、差值扩展这3种可逆信息隐藏算法,保证每个子块至少可嵌入1 bit认证码的同时,将平均可嵌入认证码的数量提高到3.6 bit,实现了图像子块独立认证与提升了篡改检测正确率;最后,考虑了3种可逆信息隐藏算法之间的连贯性与可逆性,以确保嵌入端的无效移位尽可能少,以及认证端认证信息的可靠提取。实验结果表明:对于未受篡改的图像区域,所提方法可以实现原始图像的无损恢复;对于剪贴攻击、常数攻击、拼贴攻击和随机篡改等常见的篡改攻击,所提方法的初始正确检测率较现有最优方法平均可提升5.82%~22.3%,细化处理后的正确检测率可接近100%。

基于生成对抗网络的车牌图像篡改检测数据增广

现有的篡改检测方法,主要使用基于数据驱动的深度学习模型,检测效果与训练数据的质量和数量成正比,且人工制作高质量的篡改图片费时费力。针对高质量车牌篡改图片数据量少的情况,提出一种针对车牌场景的篡改图片数据增广方法。结合车牌定位模块、车牌矫正模块、基于生成对抗网络(GAN)的图像擦除模块和文字风格迁移模块,构建一个车牌字符篡改系统,以模拟真实场景的车牌篡改流程。相较于传统篡改方法,借助GAN生成的篡改字符种类更多元化、更具备多样性。实验结果表明,使用所提系统生成的车牌篡改图片可以达到篡改区域语义高度合理,且肉眼不可分辨的视觉效果;将它作为扩充数据训练篡改检测模型,曲线下面积(AUC)提升了42.9%,F1值提升了33.0%,漏检率下降了16.6%。同时,使用所提系统生成的车牌篡改图片搭配多种数据处理方法在不同篡改检测网络上均能有效提升检测性能;使用扩充数据训练后,篡改检测网络不仅可以成功检测传统篡改方法的篡改痕迹,针对现阶段流行的生成式篡改,检测效果也明显提升。

基于改进深度神经网络的数字图像篡改检测方法

针对现有数字图像内容篡改检测方法存在的检测精度低、检测类型单一等问题,基于现有数字图像篡改技术,提出一种用于数字图像篡改检测的改进深度神经网络。利用Scharr算子提取图像的边缘信息,通过灰度共生矩阵将不同的边缘矩阵统一为相同的大小,在输入改进的深度神经网络中进行多篡改检测。通过试验与常规方法进行了比较分析,验证了所提方法的优越性。结果表明,所提出方法与常规图像篡改检测方法相比,在数字图像篡改检测中具有更优的检测准确率和检测速度,检测准确率达到98.25%,检测速度达到12.99 FPS。