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

A Short Survey on Perceptual Hash Function

The authentication of digital image has become more important as these images can be easily manipulated by using image processing tools leading to various problems such as copyright infringement and hostile tampering to the image contents. It is almost impossible to distinguish subjectively which images are original and which have been manipulated. There are several cryptographic hash functions that map the input data to short binary strings but these traditional cryptographic hash functions is not suitable for image authentication as they are very sensitive to every single bit of input data. When using a cryptographic hash function, the change of even one bit of the original data results in a radically different value. A modified image should be detected as authentic by the hash function and at the same time must be robust against incidental and legitimate modifications on multimedia data. The main aim of this paper is to present a survey of perceptual hash functions for image authentication.Keywords: Hash function, image authentication*Cite as: Arambam Neelima, Kh. Manglem Singh, “A Short Survey on Perceptual Hash Function†ADBU-J.Engg Tech, 1(2014) 0011405(8pp

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

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

Table Augmentation in Data Lakes

Data lakes are centralized repositories that store large quantities of raw, unstructured, and structured data, allowing for ad-hoc data analysis, exploratory data analysis, and machine learning. However, the lack of metadata and schema in data lakes makes it challenging to work with tabular data and find related information stored in different tables. However, it is still an open problem how efficiently retrieve these tables at large scale when the settings of a data lake holds. The thesis introduces a novel approach to table augmentation that enables efficient data integration from multiple sources in a data lake. Table augmentation involves adding new data to an existing table in a horizontal fashion (by retrieving tables that can be horizontally concatenated to a query that serves as query table). The proposed approach consists of several components, including data lakes hashing, join search, similarity, and augmentation. The proposed approach is named TASH. TASH is a framework based on a spatial index in which tables are mapped and queried. Its goal is to identify the most useful columns for subsequent machine learning tasks. The table retrieval process employs a combination of set containment search and similarity search. Candidate tables are initially identified using set containment search and then ranked based on their similarity to the query. Experimental results demonstrate that TASH can effectively identify joinable tables and select the most relevant features, thereby enabling efficient table augmentation in data lakes. This research contributes to the field of big data by providing a practical solution to the challenges of data integration and analysis in data lake environments

Iris Identification using Keypoint Descriptors and Geometric Hashing

Iris is one of the most reliable biometric trait due to its stability and randomness. Conventional recognition systems transform the iris to polar coordinates and perform well for co-operative databases. However, the problem aggravates to manifold for recognizing non-cooperative irises. In addition, the transformation of iris to polar domain introduces aliasing effect. In this thesis, the aforementioned issues are addressed by considering Noise Independent Annular Iris for feature extraction. Global feature extraction approaches are rendered as unsuitable for annular iris due to change in scale as they could not achieve invariance to ransformation and illumination. On the contrary, local features are invariant to image scaling, rotation and partially invariant to change in illumination and viewpoint. To extract local features, Harris Corner Points are detected from iris and matched using novel Dual stage approach. Harris corner improves accuracy but fails to achieve scale invariance. Further, Scale Invariant Feature Transform (SIFT) has been applied to annular iris and results are found to be very promising. However, SIFT is computationally expensive for recognition due to higher dimensional descriptor. Thus, a recently evolved keypoint descriptor called Speeded Up Robust Features (SURF) is applied to mark performance improvement in terms of time as well as accuracy. For identification, retrieval time plays a significant role in addition to accuracy. Traditional indexing approaches cannot be applied to biometrics as data are unstructured. In this thesis, two novel approaches has been developed for indexing iris database. In the first approach, Energy Histogram of DCT coefficients is used to form a B-tree. This approach performs well for cooperative databases. In the second approach, indexing is done using Geometric Hashing of SIFT keypoints. The latter indexing approach achieves invariance to similarity transformations, illumination and occlusion and performs with an accuracy of more than 98% for cooperative as well as non-cooperative databases