Image Tamper Detection Research Articles

Self-embedding framework for tamper detection and restoration of color images

Self-embedding framework for tamper detection and restoration of color images

Cryptanalysis of a Hamming Code and Logistic-Map based Pixel-Level Active Forgery Detection Scheme

In this paper, we analyze the security of a fragile watermarking scheme for tamper detection in images recently proposed by S. Prasad et al. The chaotic functions are used in the scheme to exploit its pseudo-random behavior and its sensibility to initial condition and control parameter, but despite that, security flaws have been spotted and cryptanalysis of the scheme is conducted. Experimental results shows that the scheme could not withstand the attack and watermarked images were manipulated without triggering any alarm in the extraction scheme. In this paper, two different approaches of attacks are demonstrated and conducted to break the scheme. This work falls into the context of improving the quality of the designed cryptographic schemes taking into account several cryptanalysis techniques.

Reference-free differential histogram-correlative detection of steganography: performance analysis

<span lang="EN-US">Recent <span>research has demonstrated the effectiveness of utilizing neural networks for detect tampering in images. However, because accessing a database is complex, which is needed in the classification process to detect tampering, reference-free steganalysis attracted attention. In recent work, an approach for least significant bit (LSB) steganalysis has been presented based on analyzing the derivatives of the histogram correlation. In this paper, we further examine this strategy for other steganographic methods. Detecting image tampering in the spatial domain, such as image steganography. It is found that the above approach could be applied successfully to other kinds of steganography with different orders of histogram-correlation derivatives. Also, the limits of the ratio stego-image to cover are considered, where very small ratios can escape this detection method unless </span> modified.</span>

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 detection method 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.

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. This makes 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.

Markov Matrix and Entropy based Tamper Detection Technique for Text Images

In this paper, a novel watermarking technique for the tamper detection of text images is pro- posed. Entropy of every sentence is computed and Markov matrix using the occurrences of the characters is used to generate a character pattern. Entropy and character patterns are converted to Unicode Zero Width Characters (ZWCs) by using a lookup table. The ZWCs of entropy of each sentence is embedded at the end of every sentence after terminator. ZWCs of the character patterns are embedded in the end of the text of the image. On receiver side, ZWCs are extracted and converted to numerical form using the same lookup table. Entropy of every sentence and character patterns are recalculated and compared with extracted values for tamper detection. Comparison of technique with existing state-of-art techniques shows the effectiveness of the proposed technique.

Zero-bit fragile watermarking for medical image tamper detection and recovery using RS code and lifting wavelet transform

ABSTRACT Medical images are habitually composed of Regions of Interest (ROI) and Region of Non-interest (RONI). ROI means the area containing the crucial information for diagnosis. To achieve the fidelity and integrity of the ROI, we propose a novel watermarking approach based on two great concepts zero-watermarking and Error Correcting Code (ECC). Tamper detection and recovery information are stored in RONI which accepts some visual quality degradation. Experimental results prove that our proposed approach produces a good performance in terms of imperceptibility and the ability to resist several hard attacks. The proposed scheme can recover 100% of the ROI until 15% of distortion is caused by salt and pepper noise. For multiplicative and Gaussian noise, the scheme has also a good capability of recovery (between 70% and 100%). With close to 20% of distortion, the system can recover 50% of the ROI. The proposed method has also a good capability of recovery against cropping and image processing operations.

A novel image tamper detection approach by blending forensic tools and optimized CNN: Sealion customized firefly algorithm

A novel image tamper detection approach by blending forensic tools and optimized CNN: Sealion customized firefly algorithm

Image tampering detection based on a statistical model

Image tampering detection based on a statistical model

Web and Social Media Image Forensics for News Professionals

User-generated content -commonly referred to as "eyewitness media"- has become an essential component in journalism and news reporting. Increasingly more news providers, such as news agencies, broadcasters and Web-only players have set up teams of dedicated investigators or are in the process of training parts of their journalistic workforce to gather and evaluate material from social networks and the Web. If verified, such content can be invaluable in delivering a news story. However, while source checking and verification is as old as journalism itself, the verification of digital material is a relatively young field, with protocols and assisting tools still being developed. In this work, we present our efforts towards a Web-based image verification platform. The platform, currently in its alpha stage, features image tampering detection using a number of state-of-the-art algorithms and image metadata visualization. We discuss the current strengths and limitations of the platform and the implemented state-of-the-art with respect to the specific requirements of the task, resulting from its Web-based nature and its intended use by news investigators with limited expertise in the domain of image forensics.

A self-embedding technique for tamper detection and localization of medical images for smart-health

A self-embedding technique for tamper detection and localization of medical images for smart-health

Digital Image Recovery Based on Lifting Wavelet Transform

Recently, with the wide distribution of digital media, the need for authenticating digital images was increased. Therefore, many image tamper detection and recovery algorithms were introduced in literature to detect malicious modifications and retrieve the original images. The process of detection and recovery, however, used to have complex operation which requires long processing time. In this paper, a simplified image recovery algorithm is presented by using lifting wavelet transform. In the proposed method, the approximation band is hidden inside the bits of the original image and to be retrieved without relying on source image. For images with hidden data, the average PSNR and SSIM values were 31.22 and 0.977 respectively, and images were successfully retrieved after block attack.

A logistic map based blind and fragile watermarking for tamper detection and localization in images

A logistic map based blind and fragile watermarking for tamper detection and localization in images

Perceptual hash-based coarse-to-fine grained image tampering forensics method

As an active forensic technology, perceptual image hash has important application in image content authenticity detection and integrity authentication. In this paper, we propose a hybrid-feature-based perceptual image hash method that can be used for image tampering detection and tampering localization. In the proposed method, we use the color features of image as global features, use point-based features and block-based features as local features, and combine with the structural features to generate intermediate hash code. Then we encrypt and randomize to generate the final hash code. Using this hash code, we present a coarse-to-fine grained forensics method for image tampering detection. The proposed method can realize object-level tampering localization. Abundant experimental results show that the proposed method is sensitive to content changes caused by malicious attacks, and the tampering localization precision achieves pixel level, and it is robust to a wide range of geometric distortions and content-preserving manipulations. Compared with the state-of-the-art schemes, the proposed scheme yields superior performance.

Image Forgery Detection and Localization

A human brain responds at a much faster rate to images and the information it contains. An image is considered as proof of past events that have occurred, but in today's world where editing tools are made available so easily tampering of images and hiding the original content has become too mainstream. The identification of these tampered images is very important as images are considered as vital sources of information in crime investigation and in various other fields. The image forgery detection techniques check the credibility of the image. Various research has been carried out in dealing with image forgery and tampering detection techniques, this paper highlights various the type of forgery and how they can be detected using various techniques. The fusion of various algorithms so that a complete reliable type of algorithm can be developed to deal mainly with copy-move and image splicing forgery. The copy-move and image splicing method are main focus of this paper.

Semi-fragile watermarking scheme based on perceptual hash function (PHF) for image tampering detection

The increasing performances of personal computers, as well as software of image processing, enable the easy manipulation of digital media content. Unfortunately, this easy manipulation makes the detection of changes in the multimedia content a very difficult task. Digital watermarking is among the most appropriate techniques to verify the integrity of multimedia content. In this paper, we propose a semi-fragile watermarking scheme for JPEG2000 image self-authentication that ensuring the robustness of the watermark against the compression attacks generated by the JPEG2000 encoder itself. The scheme is combined with the JPEG2000 encoder by embedding the generated watermark into the host image during the JPEG2000 compression process. To generate the watermark we suppose to use a perceptual hash function (PHF) operating on discrete wavelet coefficients of the host image. The proposed watermark generation process leads the system to verify the integrity of the image without the need to any file except for the watermarked image. The watermark is embedded during the JPEG2000 compression process after Discrete Wavelet Transform (DWT) step into the approximation sub-band coefficients of the five wavelet decomposition using Index Modulation Quantification (QIM), and can be extracted during image decoding. To prove the authentication of the image, the system compares the extracted watermark with the new watermark generated from the received image. Experimental results show that our proposed approach has not only an extremely high accuracy of tampering detection but also a relatively very high resistance against JPEG2000 compression attacks.

General Purpose Image Tampering Detection using Convolutional Neural Network and Local Optimal Oriented Pattern (LOOP)

Digital image tampering detection has been an active area of research in recent times due to the ease with which digital image can be modified to convey false or misleading information. To address this problem, several studies have proposed forensics algorithms for digital image tampering detection. While these approaches have shown remarkable improvement, most of them only focused on detecting a specific type of image tampering. The limitation of these approaches is that new forensic method must be designed for each new manipulation approach that is developed. Consequently, there is a need to develop methods capable of detecting multiple tampering operations. In this paper, we proposed a novel general purpose image tampering scheme based on CNNs and Local Optimal Oriented Pattern (LOOP) which is capable of detecting five types of image tampering in both binary and multiclass scenarios. Unlike the existing deep learning techniques which used constrained pre-processing layers to suppress the effect of image content in order to capture image tampering traces, our method uses LOOP features, which can effectively subdue the effect image content, thus, allowing the proposed CNNs to capture the needed features to distinguish among different types of image tampering. Through a number of detailed experiments, our results demonstrate that the proposed general purpose image tampering method can achieve high detection accuracies in individual and multiclass image tampering detections respectively and a comparative analysis of our results with the existing state of the arts reveals that the proposed model is more robust than most of the exiting methods.

Cloud-Based Fusion of Residual Exploitation-Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics

Cloud computing has evolved in various application areas such as medical imaging and bioinformatics. It raises the issues of privacy and tampering in the images especially related to the medical field and bioinformatics for various reasons. The digital images are quite vulnerable to be tampered by the interceptors. The credibility of individuals can transform through falsified information in the images. Image tampering detection is an approach to identifying and finding the tampered components in the image. For the efficient detection of image tampering, the sufficient number of features are required which can be achieved by a deep learning architecture-based models without manual feature extraction of functions. In this research work, we have presented and implemented a cloud-based residual exploitation-based deep learning architectures to detect whether or not an image is being tampered. The proposed approach is implemented on the publicly available benchmark MICC-F220 dataset with the k -fold cross-validation approach to avoid the overfitting problem and to evaluate the performance metrics.

Digital Image Tampering Detection Using Multilevel Local Binary Pattern Texture Descriptor

Digital images can be manipulated using the latest tools and techniques without leaving any visible traces. Image tampering detection is required to authenticate image validation. It is concluded from previous research that image tampering modifies the texture micropattern in a digital image. Therefore, texture descriptors can be applied to highlight these changes. A texture descriptor–based technique is proposed for detecting both copy-move and splicing forgery. In the proposed method, an RGB image is converted into a YCbCr image and Cb and Cr image components are extracted, as these components are more sensitive to tampering artifacts. Further, a standard deviation (STD) filter and higher-order texture descriptors are applied on Cb and Cr components. The STD filter is used to highlight important details of objects in the image. A support vector machine classifier is used to classify forged and tampered images. Support vector machine (SVM) classifier gives good results on both large- and small-image data sets. The performance is appraised in three online-available, widely used data sets: CASIA v1.0, CASIA v2.0, and Columbia. The proposed method outperforms most of the state-of-the-art methods.

Identification of copy-move and splicing based forgeries using advanced SURF and revised template matching

Various image tampering detection approaches are used to find the variations or inconsistencies in statistical image features. But still these techniques lack behind to identify copy-move and splicing based manipulations. The manipulation in digital data encourages the crimes, particularly in the domain of image processing and computer vision-based applications. Therefore, to find image forgeries, new method needs to be designed so that originality of data is authenticated in the court of law or jurisdiction. To achieve, a pixel based forgery detection framework for copy-move and splicing based forgeries is suggested in this paper. Initially, pre-processing over image data is performed to enhance the textural information. The proposed system estimates various features using enhanced SURF and template matching for the identification of fake image regions. The relevant key parameters are estimated and compared with the calculated threshold value. The evaluation is carried out using CASIA forged image dataset. The results are evaluated and compared with other existing methods through a comprehensive set of experiments. The enhanced SURF method produces a forgery detection accuracy of 97%, while template matching gives 100% forgery detection. As a whole system, the accuracy is 97.5%. Thus, the demonstrated result shows that the proposed framework attains considerably more detection accuracy compared to other state-of-art techniques.