Fragile watermarking for image authentication using dyadic walsh ordering

A digital image is subjected to the most manipulation. This is driven by the easy manipulating process through image editing software which is growing rapidly. These problems can be solved through the watermarking model as an active authentication system for the image. One of the most popular methods is Singular Value Decomposition (SVD) which has good imperceptibility and detection capabilities. Nevertheless, SVD has high complexity and can only utilize one singular matrix S, and ignore two orthogonal matrices. This paper proposes the use of the Walsh matrix with dyadic ordering to generate a new S matrix without the orthogonal matrices. The experimental results showed that the proposed method was able to reduce computational time by 22% and 13% compared to the SVD-based method and similar methods based on the Hadamard matrix respectively. This research can be used as a reference to speed up the computing time of the watermarking methods without compromising the level of imperceptibility and authentication

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

Improving the experimental analysis of tampered image detection algorithms for biometric systems

In this paper we deal with the experimental evaluation of tampered image detection algorithms. These algorithms aim at establishing if any manipulation has been carried out on a digital image. In detail, we focus on the evaluation of the CASIA Tampered Image Detection Evaluation (CASIA TIDE) public dataset of images, the de facto standard for evaluating these class of algorithms. Our analysis has been performed using the algorithm of Lin et al. for JPEG tampered image detection as benchmark. The results proved that the images of the dataset contain some statistical artifacts that may help the detection process. To confirm this, we first used this dataset to evaluate the performance of the Lin et al. algorithm. According to our results, the considered algorithm performs very well on this dataset. Some variants of the original algorithm have been developed expressly tuned on these artifacts. These variants performed better than their original counterpart. Then a new unbiased dataset has been assembled and a new set of experiments has been executed with these images. The results showed that the performance of the algorithm and its variants radically decreased, proving that the CASIA TIDE statistical artifacts cause interferences on the detection process. This problem is particularly important in the biometric field, because many image-based biometric systems rely on the assumption that input images have not been manipulated. Indeed, a faithful experimental evaluation must be based on unbiased input dataset to get well founded results. Therefore, the selection of a reliable image tampering detection algorithm is crucial. A preliminary version of this work has been presented in Cattaneo and Roscigno (2014) [6]

Improving the experimental analysis of tampered image detection algorithms for biometric systems

Abstract In this paper we deal with the experimental evaluation of tampered image detection algorithms. These algorithms aim at establishing if any manipulation has been carried out on a digital image. In detail, we focus on the evaluation of the CASIA Tampered Image Detection Evaluation (CASIA TIDE) public dataset of images, the de facto standard for evaluating these class of algorithms. Our analysis has been performed using the algorithm of Lin et al. for JPEG tampered image detection as benchmark. The results proved that the images of the dataset contain some statistical artifacts that may help the detection process. To confirm this, we first used this dataset to evaluate the performance of the Lin et al. algorithm. According to our results, the considered algorithm performs very well on this dataset. Some variants of the original algorithm have been developed expressly tuned on these artifacts. These variants performed better than their original counterpart. Then a new unbiased dataset has been assembled and a new set of experiments has been executed with these images. The results showed that the performance of the algorithm and its variants radically decreased, proving that the CASIA TIDE statistical artifacts cause interferences on the detection process. This problem is particularly important in the biometric field, because many image-based biometric systems rely on the assumption that input images have not been manipulated. Indeed, a faithful experimental evaluation must be based on unbiased input dataset to get well founded results. Therefore, the selection of a reliable image tampering detection algorithm is crucial. A preliminary version of this work has been presented in Cattaneo and Roscigno (2014) [6]