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

Detection of Nonaligned Double JPEG Compression Based on Integer Periodicity Maps

In this paper, a simple yet reliable algorithm to detect the presence of nonaligned double JPEG compression (NA-JPEG) in compressed images is proposed. The method evaluates a single feature based on the integer periodicity of the blockwise discrete cosine transform (DCT) coefficients when the DCT is computed according to the grid of the previous JPEG compression. Even if the proposed feature is computed relying only on DC coefficient statistics, a simple threshold detector can classify NA-JPEG images with improved accuracy with respect to existing methods and on smaller image sizes, without resorting to a properly trained classifier. Moreover, the proposed scheme is able to accurately estimate the grid shift and the quantization step of the DC coefficient of the primary JPEG compression, allowing one to perform a more detailed analysis of possibly forged image

An Overview on Image Forensics

The aim of this survey is to provide a comprehensive overview of the state of the art in the area of image forensics. These techniques have been designed to identify the source of a digital image or to determine whether the content is authentic or modified, without the knowledge of any prior information about the image under analysis (and thus are defined as passive). All these tools work by detecting the presence, the absence, or the incongruence of some traces intrinsically tied to the digital image by the acquisition device and by any other operation after its creation. The paper has been organized by classifying the tools according to the position in the history of the digital image in which the relative footprint is left: acquisition-based methods, coding-based methods, and editing-based schemes

Image Forgery Localization via Block-Grained Analysis of JPEG Artifacts

In this paper, we propose a forensic algorithm to discriminate between original and forged regions in JPEG images, under the hypothesis that the tampered image presents a double JPEG compression, either aligned (A-DJPG) or non-aligned (NA-DJPG). Unlike previous approaches, the proposed algorithm does not need to manually select a suspect region in order to test the presence or the absence of double compression artifacts. Based on an improved and unified statistical model characterizing the artifacts that appear in the presence of both A-DJPG or NA-DJPG, the proposed algorithm automatically computes a likelihood map indicating the probability for each $8 \times 8$ discrete cosine transform block of being doubly compressed. The validity of the proposed approach has been assessed by evaluating the performance of a detector based on thresholding the likelihood map, considering different forensic scenarios. The effectiveness of the proposed method is also confirmed by tests carried on realistic tampered images. An interesting property of the proposed Bayesian approach is that it can be easily extended to work with traces left by other kinds of processin

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

Boosting Image Forgery Detection using Resampling Features and Copy-move analysis

Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting splicing and resampling, copy-move detection algorithms excel in detecting cloning and region removal. In this paper, we combine these complementary approaches in a way that boosts the overall accuracy of image manipulation detection. We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. Experimental results on various datasets including the 2017 NIST Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and tampered images shows that there is a consistent increase of 8%-10% in detection rates, when copy-move algorithm is combined with different resampling detection algorithms

Statistical Feature based Blind Classifier for JPEG Image Splice Detection

Digital imaging, image forgery and its forensics have become an established field of research now days. Digital imaging is used to enhance and restore images to make them more meaningful while image forgery is done to produce fake facts by tampering images. Digital forensics is then required to examine the questioned images and classify them as authentic or tampered. This paper aims to design and implement a blind classifier to classify original and spliced Joint Photographic Experts Group (JPEG) images. Classifier is based on statistical features obtained by exploiting image compression artifacts which are extracted as Blocking Artifact Characteristics Matrix. The experimental results have shown that the proposed classifier outperforms the existing one. It gives improved performance in terms of accuracy and area under curve while classifying images. It supports .bmp and .tiff file formats and is fairly robust to noise

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