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

Effectiveness of deep learning architecture for pixel-based image forgery detection

Digital image forgery or forgery is easy to do nowadays. Verification of the authenticity of images is important to protect the integrity of the images from being misused. The use of a deep learning approach is state-of-the-art in solving cases of pattern recognition, the one is image data classification. In this study, image forgery detection was carried out using a deep learning-based method, the Convolutional Neural Network (CNN). The analysis of the different architecture of CNN has been done to show the effectiveness of each architecture. Two architectures were tested to know which one is more effective, architecture 1 has three convolution and pooling layers with 256 × 256 × 3 image input. While the other architecture has two convolution layers and pooling with 128 × 128 × 3 image input. The results show that the accuracy rate of the image forgery detection model in each architecture is around 80%. However, the validation accuracy is not more than 70%

A deep multimodal system for provenance filtering with universal forgery detection and localization

[EN] Traditional multimedia forensics techniques inspect images to identify, localize forged regions and estimate forgery methods that have been applied. Provenance filtering is the research area that has been evolved recently to retrieve all the images that are involved in constructing a morphed image in order to analyze an image, completely forensically. This task can be performed in two stages: one is to detect and localize forgery in the query image, and the second integral part is to search potentially similar images from a large pool of images. We propose a multimodal system which covers both steps, forgery detection through deep neural networks(CNN) followed by part based image retrieval. Classification and localization of manipulated region are performed using a deep neural network. InceptionV3 is employed to extract key features of the entire image as well as for the manipulated region. Potential donors and nearly duplicates are retrieved by using the Nearest Neighbour Algorithm. We take the CASIA-v2, CoMoFoD and NIST 2018 datasets to evaluate the proposed system. Experimental results show that deep features outperform low-level features previously used to perform provenance filtering with achieved Recall@50 of 92.8%.Jabeen, S.; Khan, UG.; Iqbal, R.; Mukherjee, M.; Lloret, J. (2021). A deep multimodal system for provenance filtering with universal forgery detection and localization. Multimedia Tools and Applications. 80(11):17025-17044. https://doi.org/10.1007/s11042-020-09623-w1702517044801

D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization

Recently, many detection methods based on convolutional neural networks (CNNs) have been proposed for image splicing forgery detection. Most of these detection methods focus on the local patches or local objects. In fact, image splicing forgery detection is a global binary classification task that distinguishes the tampered and non-tampered regions by image fingerprints. However, some specific image contents are hardly retained by CNN-based detection networks, but if included, would improve the detection accuracy of the networks. To resolve these issues, we propose a novel network called dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs an unfixed encoder and a fixed encoder. The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network. This dual-encoder is followed by a spatial pyramid global-feature extraction module that expands the global insight of D-Unet for classifying the tampered and non-tampered regions more accurately. In an experimental comparison study of D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in image-level and pixel-level detection, without requiring pre-training or training on a large number of forgery images. Moreover, it was stably robust to different attacks.Comment: 13 pages, 13 figure

Re-compression Based JPEG Forgery Detection and Localization with Optimal Reconstruction

In today’s media–saturated society, digital images act as the primary carrier for majority of information that flows around us. However, because of the advent of highly sophisticated easy–to–use image processing tools, modifying images has become easy. Joint Photographic Experts Group (JPEG) is the most widely used format, prevalent today as a world–wide standard, for compression and storage of digital images. Almost all present–day digital cameras use the JPEG format for image acquisition and storage, due to its efficient compression features and optimal space requirement. In this propose work we aim to detect malicious tampering of JPEG images, and subsequently reconstruct the forged image optimally. We deal with lossy JPEG image format in this paper, which is more widely adopted compared to its lossless counter–part. The proposed technique is capable of detecting single as well as multiple forged regions in a JPEG image. We aim to achieve optimal reconstruction since the widely used JPEG being a lossy technique, under no condition would allow 100% reconstruction. The proposed reconstruction is optimal in the sense that we aim to obtain a form of the image, as close to its original form as possible, apart from eliminating the effects of forgery from the image. In this work, we exploit the inherent characteristics of JPEG compression and re–compression, for forgery detection and reconstruction of JPEG images. To prove the efficiency of our proposed technique we compare it with the other JPEG forensic techniques and using quality metric measures we assess the visual quality of the reconstructed image

Image statistical frameworks for digital image forensics

The advances of digital cameras, scanners, printers, image editing tools, smartphones, tablet personal computers as well as high-speed networks have made a digital image a conventional medium for visual information. Creation, duplication, distribution, or tampering of such a medium can be easily done, which calls for the necessity to be able to trace back the authenticity or history of the medium. Digital image forensics is an emerging research area that aims to resolve the imposed problem and has grown in popularity over the past decade. On the other hand, anti-forensics has emerged over the past few years as a relatively new branch of research, aiming at revealing the weakness of the forensic technology. These two sides of research move digital image forensic technologies to the next higher level. Three major contributions are presented in this dissertation as follows. First, an effective multi-resolution image statistical framework for digital image forensics of passive-blind nature is presented in the frequency domain. The image statistical framework is generated by applying Markovian rake transform to image luminance component. Markovian rake transform is the applications of Markov process to difference arrays which are derived from the quantized block discrete cosine transform 2-D arrays with multiple block sizes. The efficacy and universality of the framework is then evaluated in two major applications of digital image forensics: 1) digital image tampering detection; 2) classification of computer graphics and photographic images. Second, a simple yet effective anti-forensic scheme is proposed, capable of obfuscating double JPEG compression artifacts, which may vital information for image forensics, for instance, digital image tampering detection. Shrink-and-zoom (SAZ) attack, the proposed scheme, is simply based on image resizing and bilinear interpolation. The effectiveness of SAZ has been evaluated over two promising double JPEG compression schemes and the outcome reveals that the proposed scheme is effective, especially in the cases that the first quality factor is lower than the second quality factor. Third, an advanced textural image statistical framework in the spatial domain is proposed, utilizing local binary pattern (LBP) schemes to model local image statistics on various kinds of residual images including higher-order ones. The proposed framework can be implemented either in single- or multi-resolution setting depending on the nature of application of interest. The efficacy of the proposed framework is evaluated on two forensic applications: 1) steganalysis with emphasis on HUGO (Highly Undetectable Steganography), an advanced steganographic scheme embedding hidden data in a content-adaptive manner locally into some image regions which are difficult for modeling image statics; 2) image recapture detection (IRD). The outcomes of the evaluations suggest that the proposed framework is effective, not only for detecting local changes which is in line with the nature of HUGO, but also for detecting global difference (the nature of IRD)