Content Authentication and Forge Detection using Perceptual Hash for Image Database

Popularity of digital technology is very high. Number of digital images are being created and stored every day. This introduces a problem for managing image databases and security of images. One cannot determine if an image already exists in a database without exhaustively searching through all the entries. Further complication arises from the fact that two images appearing identical to the human eye may have distinct digital representations, making it difficult to compare a pair of images. Also the security of database server is questionable. The proposed framework provides the content authentication and forges detection of image. This can be done by generating perceptual image hash using SIFT algorithm, Perceptual image hash also known as perceptual image signature. It has been proposed as a primitive method to solve problems of image content authentication. The perceptual image hash is generated by using the perceptual features that are in accordance with human’s visual characteristics. It allows tampering of images to permissible extent e.g. improving slight brightness or contrast in image. A perceptual image hash is expected to be able to survive unintentional distortion and reject malicious tampering within an acceptable extend .Therefore it provides a more efficient approach to analyzing changes of image perceptual content and make sure database server is authenticated or not

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

Abstract Images Have Different Levels of Retrievability Per Reverse Image Search Engine

Much computer vision research has focused on natural images, but technical documents typically consist of abstract images, such as charts, drawings, diagrams, and schematics. How well do general web search engines discover abstract images? Recent advancements in computer vision and machine learning have led to the rise of reverse image search engines. Where conventional search engines accept a text query and return a set of document results, including images, a reverse image search accepts an image as a query and returns a set of images as results. This paper evaluates how well common reverse image search engines discover abstract images. We conducted an experiment leveraging images from Wikimedia Commons, a website known to be well indexed by Baidu, Bing, Google, and Yandex. We measure how difficult an image is to find again (retrievability), what percentage of images returned are relevant (precision), and the average number of results a visitor must review before finding the submitted image (mean reciprocal rank). When trying to discover the same image again among similar images, Yandex performs best. When searching for pages containing a specific image, Google and Yandex outperform the others when discovering photographs with precision scores ranging from 0.8191 to 0.8297, respectively. In both of these cases, Google and Yandex perform better with natural images than with abstract ones achieving a difference in retrievability as high as 54\% between images in these categories. These results affect anyone applying common web search engines to search for technical documents that use abstract images.Comment: 20 pages; 7 figures; to be published in the proceedings of the Drawings and abstract Imagery: Representation and Analysis (DIRA) Workshop from ECCV 202

A quick search method for audio signals based on a piecewise linear representation of feature trajectories

This paper presents a new method for a quick similarity-based search through long unlabeled audio streams to detect and locate audio clips provided by users. The method involves feature-dimension reduction based on a piecewise linear representation of a sequential feature trajectory extracted from a long audio stream. Two techniques enable us to obtain a piecewise linear representation: the dynamic segmentation of feature trajectories and the segment-based Karhunen-L\'{o}eve (KL) transform. The proposed search method guarantees the same search results as the search method without the proposed feature-dimension reduction method in principle. Experiment results indicate significant improvements in search speed. For example the proposed method reduced the total search time to approximately 1/12 that of previous methods and detected queries in approximately 0.3 seconds from a 200-hour audio database.Comment: 20 pages, to appear in IEEE Transactions on Audio, Speech and Language Processin