Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

A robust forgery detection method for copy-move and splicing attacks in images

Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy-move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors' physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.This research was funded by Research Priority Area (RPA) scholarship of Federation University Australia

Review on local binary patterns variants as texture descriptors for copy-move forgery detection

Past decades had seen the concerned by researchers in authenticating the originality of an image as the result of advancement in computer technology. Many methods have been developed to detect image forgeries such as copy-move, splicing, resampling and et cetera. The most common type of image forgery is copy-move where the copied region is pasted on the same image. The existence of high similarity in colour and textures of both copied and pasted images caused the detection of the tampered region to be very difficult. Additionally, the existence of post-processing methods makes it more challenging. In this paper, Local Binary Pattern (LBP) variants as texture descriptors for copy-move forgery detection have been reviewed. These methods are discussed in terms of introduction and methodology in copy-move forgery detection. These methods are also compared in the discussion section. Finally, their strengths and weaknesses are summarised, and some future research directions were pointed out

Detecting splicing and copy-move attacks in color images

Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 201

Novel Methods for Forensic Multimedia Data Analysis: Part I

The increased usage of digital media in daily life has resulted in the demand for novel multimedia data analysis techniques that can help to use these data for forensic purposes. Processing of such data for police investigation and as evidence in a court of law, such that data interpretation is reliable, trustworthy, and efficient in terms of human time and other resources required, will help greatly to speed up investigation and make investigation more effective. If such data are to be used as evidence in a court of law, techniques that can confirm origin and integrity are necessary. In this chapter, we are proposing a new concept for new multimedia processing techniques for varied multimedia sources. We describe the background and motivation for our work. The overall system architecture is explained. We present the data to be used. After a review of the state of the art of related work of the multimedia data we consider in this work, we describe the method and techniques we are developing that go beyond the state of the art. The work will be continued in a Chapter Part II of this topic

Edge-texture feature based image forgery detection with cross dataset evaluation

A digital image is a rich medium of information. The development of user-friendly image editing tools has given rise to the need for image forensics. The existing methods for the investigation of the authenticity of an image perform well on a limited set of images or certain datasets but do not generalize well across different datasets. The challenge of image forensics is to detect the traces of tampering which distorts the texture patterns. A method for image forensics is proposed, which employs Discriminative robust local binary patterns (DRLBP) for encoding tampering traces and a support vector machine (SVM) for decision making. In addition, to validate the generalization of the proposed method, a new dataset is developed that consists of historic images, which have been tampered with by professionals. Extensive experiments were conducted using the developed dataset as well as the public domain benchmark datasets; the results demonstrate the robustness and effectiveness of the proposed method for tamper detection and validate its cross-dataset generalization. Based on the experimental results, directions are suggested that can improve dataset collection as well as algorithm evaluation protocols. More broadly, discussion in the community is stimulated regarding the very important, but largely neglected, issue of the capability of image forgery detection algorithms to generalize to new test data

Image splicing detection scheme using adaptive threshold mean ternary pattern descriptor

The rapid growth of image editing applications has an impact on image forgery cases. Image forgery is a big challenge in authentic image identification. Images can be readily altered using post-processing effects, such as blurring shallow depth, JPEG compression, homogenous regions, and noise to forge the image. Besides, the process can be applied in the spliced image to produce a composite image. Thus, there is a need to develop a scheme of image forgery detection for image splicing. In this research, suitable features of the descriptors for the detection of spliced forgery are defined. These features will reduce the impact of blurring shallow depth, homogenous area, and noise attacks to improve the accuracy. Therefore, a technique to detect forgery at the image level of the image splicing was designed and developed. At this level, the technique involves four important steps. Firstly, convert colour image to three colour channels followed by partition of image into overlapping block and each block is partitioned into non-overlapping cells. Next, Adaptive Thresholding Mean Ternary Pattern Descriptor (ATMTP) is applied on each cell to produce six ATMTP codes and finally, the tested image is classified. In the next part of the scheme, detected forgery object in the spliced image involves five major steps. Initially, similarity among every neighbouring district is computed and the two most comparable areas are assembled together to the point that the entire picture turns into a single area. Secondly, merge similar regions according to specific state, which satisfies the condition of fewer than four pixels between similar regions that lead to obtaining the desired regions to represent objects that exist in the spliced image. Thirdly, select random blocks from the edge of the binary image based on the binary mask. Fourthly, for each block, the Gabor Filter feature is extracted to assess the edges extracted of the segmented image. Finally, the Support Vector Machine (SVM) is used to classify the images. Evaluation of the scheme was experimented using three sets of standard datasets, namely, the Institute of Automation, Chinese Academy of Sciences (CASIA) version TIDE 1.0 and 2.0, and Columbia University. The results showed that, the ATMTP achieved higher accuracy of 98.95%, 99.03% and 99.17% respectively for each set of datasets. Therefore, the findings of this research has proven the significant contribution of the scheme in improving image forgery detection. It is recommended that the scheme be further improved in the future by considering geometrical perspective