Visible to the public Biblio

Filters: Author is Raj, Jeberson Retna  [Clear All Filters]
2022-06-09
Sujatha, G., Raj, Jeberson Retna.  2021.  Digital Data Identification for Deduplication Process using Cryptographic Hashing Techniques. 2021 International Conference on Intelligent Technologies (CONIT). :1–4.
The cloud storage system is a very big boon for the organizations and individuals who are all in the need of storage space to accommodate huge volume of digital data. The cloud storage space can handle various types of digital data like text, image, video and audio. Since the storage space can be shared among different users, it is possible to have duplicate copies of data in the storage space. An efficient mechanism is required to identify the digital data uniquely in order to check the duplicity. There are various ways by which the digital data can be identified. One among such technique is hash-based identification. Using cryptographic hashing algorithms, every data can be uniquely identified. The unique property of hashing algorithm helps to identify the data uniquely. In this research work, we are going to discuss the advantage of using cryptographic hashing algorithm for digital data identification and the comparison of various hashing algorithms.
2022-03-14
R, Padmashri., Srinivasulu, Senduru, Raj, Jeberson Retna, J, Jabez., Gowri, S..  2021.  Perceptual Image Hashing Using Surffor Feature Extraction and Ensemble Classifier. 2021 3rd International Conference on Signal Processing and Communication (ICPSC). :41—44.

Image hash regimes have been widely used for authenticating content, recovery of images and digital forensics. In this article we propose a new algorithm for image haunting (SSL) with the most stable key points and regional features, strong against various manipulation of content conservation, including multiple combinatorial manipulations. In order to extract most stable keypoint, the proposed algorithm combines the Speed Up Robust Features (SURF) with Saliency detection. The keyboards and characteristics of the local area are then combined in a hash vector. There is also a sperate secret key that is randomly given for the hash vector to prevent an attacker from shaping the image and the new hash value. The proposed hacking algorithm shows that similar or initial images, which have been individually manipulated, combined and even multiple manipulated contents, can be visently identified by experimental result. The probability of collision between hacks of various images is almost nil. Furthermore, the key-dependent security assessment shows the proposed regime safe to allow an attacker without knowing the secret key not to forge or estimate the right havoc value.