Image Hash Minimization for Tamper Detection

Tamper detection using image hash is a very common problem of modern days. Several research and advancements have already been done to address this problem. However, most of the existing methods lack the accuracy of tamper detection when the tampered area is low, as well as requiring long image hashes. In this paper, we propose a novel method objectively to minimize the hash length while enhancing the performance at low tampered area.Comment: Published at the 9th International Conference on Advances in Pattern Recognition, 201

Robust hashing for image authentication using quaternion discrete Fourier transform and log-polar transform

International audienceIn this work, a novel robust image hashing scheme for image authentication is proposed based on the combination of the quaternion discrete Fourier transform (QDFT) with the log-polar transform. QDFT offers a sound way to jointly deal with the three channels of color images. The key features of the present method rely on (i) the computation of a secondary image using a log-polar transform; and (ii) the extraction from this image of low frequency QDFT coefficients' magnitude. The final image hash is generated according to the correlation of these magnitude coefficients and is scrambled by a secret key to enhance the system security. Experiments were conducted in order to analyze and identify the most appropriate parameter values of the proposed method and also to compare its performance to some reference methods in terms of receiver operating characteristics curves. The results show that the proposed scheme offers a good sensitivity to image content alterations and is robust to the common content-preserving operations, and especially to large angle rotation operations

Hough transform generated strong image hashing scheme for copy detection

The rapid development of image editing software has resulted in widespread unauthorized duplication of original images. This has given rise to the need to develop robust image hashing technique which can easily identify duplicate copies of the original images apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on discrete wavelet transform and Hough transform, which is robust to large number of image processing attacks including shifting and shearing. The input image is initially pre-processed to remove any kind of minor effects. Discrete wavelet transform is then applied to the pre-processed image to produce different wavelet coefficients from which different edges are detected by using a canny edge detector. Hough transform is finally applied to the edge-detected image to generate an image hash which is used for image identification. Different experiments were conducted to show that the proposed hashing technique has better robustness and discrimination performance as compared to the state-of-the-art techniques. Normalized average mean value difference is also calculated to show the performance of the proposed technique towards various image processing attacks. The proposed copy detection scheme can perform copy detection over large databases and can be considered to be a prototype for developing online real-time copy detection system

Using Context and Interactions to Verify User-Intended Network Requests

Client-side malware can attack users by tampering with applications or user interfaces to generate requests that users did not intend. We propose Verified Intention (VInt), which ensures a network request, as received by a service, is user-intended. VInt is based on "seeing what the user sees" (context). VInt screenshots the user interface as the user interacts with a security-sensitive form. There are two main components. First, VInt ensures output integrity and authenticity by validating the context, ensuring the user sees correctly rendered information. Second, VInt extracts user-intended inputs from the on-screen user-provided inputs, with the assumption that a human user checks what they entered. Using the user-intended inputs, VInt deems a request to be user-intended if the request is generated properly from the user-intended inputs while the user is shown the correct information. VInt is implemented using image analysis and Optical Character Recognition (OCR). Our evaluation shows that VInt is accurate and efficient

A Review of Hashing based Image Copy Detection Techniques

Images are considered to be natural carriers of information, and a large number of images are created, exchanged and are made available online. Apart from creating new images, the availability of number of duplicate copies of images is a critical problem. Hashing based image copy detection techniques are a promising alternative to address this problem. In this approach, a hash is constructed by using a set of unique features extracted from the image for identification. This article provides a comprehensive review of the state-of-the-art image hashing techniques. The reviewed techniques are categorized by the mechanism used and compared across a set of functional & performance parameters. The article finally highlights the current issues faced by such systems and possible future directions to motivate further research work

Robust image alignment for tampering detection

The widespread use of classic and newest technologies available on Internet (e.g., emails, social networks, digital repositories) has induced a growing interest on systems able to protect the visual content against malicious manipulations that could be performed during their transmission. One of the main problems addressed in this context is the authentication of the image received in a communication. This task is usually performed by localizing the regions of the image which have been tampered. To this aim the aligned image should be first registered with the one at the sender by exploiting the information provided by a specific component of the forensic hash associated to the image. In this paper we propose a robust alignment method which makes use of an image hash component based on the Bag of Features paradigm. The proposed signature is attached to the image before transmission and then analyzed at destination to recover the geometric transformations which have been applied to the received image. The estimator is based on a voting procedure in the parameter space of the model used to recover the geometric transformation occurred into the manipulated image. The proposed image hash encodes the spatial distribution of the image features to deal with highly textured and contrasted tampering patterns. A block-wise tampering detection which exploits an histograms of oriented gradients representation is also proposed. A non-uniform quantization of the histogram of oriented gradient space is used to build the signature of each image block for tampering purposes. Experiments show that the proposed approach obtains good margin of performances with respect to state-of-the art methods