Digital Forensic Technique for Multiple Compression based JPEG Forgery

In today's digital world digital multimedia like, images, voice-notes and videos etc., are the major source of information/data exchange. The authenticity of these multimedia is greatly vital in the legitimate business, media world and broadcast industry. However, with enormous multiplication of ease, simple-to-utilize data manipulation tools and softwares lead to the faithfulness of digital images is in question. In our work, we propose a technique to identify digital forgery or tampering in JPEG (Joint Photographic Experts Group) images which are based on multiple compression factor. We are dealing with the JPEG images on the grounds because JPEG is the standard storage format used in almost all present day digital devices like digital camera, camcorder, mobile devices and other image acquisition devices. JPEG compresses a image to the best compression in-order to manage the storage requirement. JPEG is a lossy compression standard. At the point when an assailant or criminal modifies some region/part of a JPEG image by any image processing tools and save it, the modified region of the image is doubly-compressed. In our work, we exploit this multiple compression in JPEG images to distinguish digital forgery or falsification

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

Forensic Technique for Detection of Image Forgery

Todays digital image plays an important role in all areas such as baking, communication, business etc. Due to the availability of manipulation software it is very easy to manipulate the original image. The contents in an original image can be copy-paste to hide some information or to create tampering. The new area introduces to detect the forgery is an image forensic. In this paper proposes the new image forensic technique to detect the presence of forgery in the compressed images and in other format images. The proposed method is based on the no subsampled contoured transform (NSCT). The proposed method is made up of three parts as preprocessing, nsct transform and forgery detection. The proposed forensic method is flexible, multiscale, multidirectional, and image decomposition is shift invariant that can be efficiently implemented via the à trous algorithm. The proposed a design framework based on the mapping approach. This method allows for a fast implementation based on a lifting or ladder structure. The proposed method ensures that the frame elements are regular, symmetric, and the frame is close to a tight one. The NSCT compares with and dct method in this paper

Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64x64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.Comment: Submitted to Journal of Visual Communication and Image Representation (first submission: March 20, 2017; second submission: August 2, 2017