Digital forgery estimation into dct domain: a critical analysis

Proceedings of the First ACM workshop on Multimedia in forensics - MiFor '09, 2009

One of the key characteristics of digital images with a discrete representation is its pliability to manipulation. Recent trends in the field of unsupervised detection of digital forgery includes several advanced strategies devoted to reveal anomalies just considering several aspects of multimedia content. One of the promising approach, among others, considers the possibility to exploit the statistical distribution of DCT coefficients in order to reveal the irregularities due to the presence of a superimposed signal over the original one (e.g., copy and paste). As recently proved the ratio between the quantization tables used to compress the signal before and after the malicious forgery alter the histograms of the DCT coefficients especially for some basis that are close in terms of frequency content. In this work we analyze in more details the performances of existing approaches evaluating their effectiveness by making use of different input datasets with respect to resolution size, compression ratio and just considering different kind of forgeries (e.g., presence of duplicate regions or images composition). We also present possible post-processing techniques able to manipulate the forged image just to reduce the performance of the current state-of-art solution. Finally we conclude the papers providing future improvements devoted to increase robustness and reliability of forgery detection into DCT domain.

In this paper, we propose a statistical test to discriminate between original and forged regions in JPEG images, under the hypothesis that the former are doubly compressed while the latter are singly compressed. New probability models for the DCT coefficients of singly and doubly compressed regions are proposed, together with a reliable method for estimating the primary quantization factor in the case of double compression. Based on such models, the probability for each DCT block to be forged is derived. Experimental results demonstrate a better discriminating behavior with respect to previously proposed methods.

2016

Now a day's images are tampered easily because availability of powerful image processing software and improvement of human computer knowledge. Manipulation of digital

IEEE Access

Various manipulations on JPEG images introduce single and multiple compression artifacts for forged and unmodified areas respectively. Based on the statistical analysis of JPEG compression cycle and on the finite mixture paradigm, we propose in this paper a modeling framework for AC DCT coefficients of such tampered JPEG images. Its accuracy is numerically assessed using the Kullback-Leibler divergence on the basis of a tampered JPEG image dataset built from six well-known uncompressed color image databases. To illustrate the framework utility, an application in image forgery localization is proposed. By formulating the localization as a clustering problem, we use the plug-in Bayes rule combined with a simple EM algorithm to distinguish between forged and unmodified areas. Numerous experiments show that, when the quality factor of final JPEG compression is high enough, the proposed modeling framework yields higher localization performances in terms of F 1-score than prior art regardless of divers local manipulations. INDEX TERMS DCT coefficients analysis, EM algorithm, forgery localization, multiple JPEG compression, statistical image models, tampered JPEG images.

With rapid advances in digital information processing systems, and more specifically in digital image processing software, there is a widespread development of advanced tools and techniques for digital image forgery. One of the techniques most commonly used is the Copy-move forgery which proceeds by copying a part of an image and pasting it into the same image, in order to maliciously hide an object or a region. In this paper, we propose a method to detect this specific kind of counterfeit. Firstly, the color image is converted from RGB color space to YCbCr color space and then the R, G, B and Y-component are splitted into fixed-size overlapping blocks and, features are extracted from the R, G and B-components image blocks on one hand and on the other, from the DCT representation of the R, G, B and Ycomponent image block. The feature vectors obtained are then lexicographically sorted to make similar image blocks neighbors and duplicated image blocks are identified using Euclidean distance as similarity criterion. Experimental results showed that the proposed method can detect the duplicated regions when there is more than one copy move forged area in the image and even in case of slight rotations, JPEG compression, shift, scale, blur and noise addition.

2006

The steady improvement in image/video editing techniques has enabled people to synthesize realistic images/videos conveniently. Some legal issues may occur when a doctored image cannot be distinguished from a real one by visual examination. Realizing that it might be impossible to develop a method that is universal for all kinds of images and JPEG is the most frequently used image format, we propose an approach that can detect doctored JPEG images and further locate the doctored parts, by examining the double quantization effect hidden among the DCT coefficients. Up to date, this approach is the only one that can locate the doctored part automatically. And it has several other advantages: the ability to detect images doctored by different kinds of synthesizing methods (such as alpha matting and inpainting, besides simple image cut/paste), the ability to work without fully decompressing the JPEG images, and the fast speed. Experiments show that our method is effective for JPEG images, especially when the compression quality is high.

2013

The use of digital photography has increased over the past few years, a trend which opens the door for new and creative ways to forge images. Now a day's several software's are available that are used to manipulate image so that the image is look like as original. Images are used as authenticated proof for any crime and if these image does not remain genuine than it will create a problem. Detecting these types of forgeries has become serious problem at present. To determine whether a digital image is original or doctored is a big challenge. To find the marks of tampering in a digital image is a challenging task. A copy-move image forgery is done either for hiding some image entity, or adding more minutiae resulting in forgery. In both the case, image reliability is lost. Although this technology brings many advantages but it can be used as a confusing tool for hiding facts and evidences. In this paper we detect region duplication forgery by applying Discrete Cosine Transform. We divide the image into overlapping blocks and then search for the duplicated blocks in the image.

A digital image plays a very crucial role for an insurance claim, as illustrative information for a news item and as evidence in judiciary system. Nevertheless, the development of effective image editing tools that effortlessly change the image contents without leaving any visible indications of such alterations makes the genuineness of the digital image suffer from dangerous threats. This has led to demonstration and proposal of various methods to check that the digital images are genuine. To detect the digital image forgery, active methods require pre-embedding of a digital signature or watermark. Generally, all digital cameras can embed such watermark or signature and thus the need of passive methods that depend completely on the features of the digital image were required. There are various passive techniques that exist and meet these difficulties, but there are no satisfactory solutions so far. This paper proposes a passive technique for Image Forgery Detection system that is designed to detect the most common types of forgery like, splicing and copy-move. Image splicing is most common type of forgery, in which forgery is carried out through copying a small part from one base image and pasting to some other image. Whereas in copy-move forgery, copied part is pasted somewhere else in the same base image to either hide or add objects. The proposed system in this work is established on Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT). Firstly, the chrominance component of original input image is divided up into overlapping blocks. After that, for each block, Local Binary Pattern (LBP) is computed and modified into frequency domain using 2 Dimensional Discrete Cosine Transform (DCT). At last, Standard Deviations are computed for frequency coefficients of all blocks respectively and hence used as the features. A Support Vector Machine (SVM) is utilized for classification. Experimental results of different benchmark image forgery databases demonstrate that the detection accuracy of proposed technique in this work is up to 89%. MATLAB R2014b tool is used to implement the proposed system.

International Journal of Image Graphics and Signal Processing, 2015

Copy move forgery detection is a very popular research area and a lot of methods have been suggested by researchers. However, every method has its own merits and weaknesses and hence, new techniques are being continuously devised and analyzed. There are many post processing operations used by the manipulators to obstruct the forgery detection techniques. One such operation is changing the contrast of the whole image or copy moved regions, which many existing methods fail to address. A novel method using binary discrete cosine transform vectors is proposed to detect copy move forgery in the presence of contrast changes. The image is divided into overlapping blocks and DCT coefficients are calculated for these blocks. Feature vectors are created from these blocks using signs of the DCT coefficients. Coefficient of correlation is used to match resulting binary vectors. The experiments show that the proposed method is able to detect copy move forgery in presence of contrast changes. The proposed method is also invariant to other post processing operations like Gaussian noise, JPEG compression and little rotation and scaling.

International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016

Nowadays, as various image manipulation tools are available very easily. Any person having a little knowledge about these tools can doctor the available images. So digital images are no longer trusted. Computer graphics and digital photography have made the tampering over image easy to commit but hard to detect. Although various image forgery techniques are available but copy-move image forgery is one of the most hard to detect image forgery. In Copy-Move image forgery a segment from the original image is copied and after performing some manipulation over that, segment is pasted at some other location on the same image. This forgery is intended to hide noticeable information shown by the image or for adding information in original image to convey a wrong message. We cannot identify such forgery on the basis of incompatibilities present in an image because the copied segment is taken from the same image so the properties like noise, blur, texture, color palette remain similar to the original image. So, copy-move image forgery is a serious threat to Image forensic Investigators. Researchers have developed several methods for detecting such kind of forgery based on exhaustive search and block based methods. Block based method is more successful in detecting such kind of forgery due to its speed and less complexity. In this paper we discuss forgery detection techniques based on Discrete Cosine Transform and Discrete Wavelet Transform.