Authors:
Jinse Shin
and
Christoph Ruland
Affiliation:
University of Siegen, Germany
Keyword(s):
Content based Image Authentication, Perceptual Image Hashing, Tamper Detection.
Related
Ontology
Subjects/Areas/Topics:
Data Engineering
;
Data Integrity
;
Databases and Data Security
;
Digital Forensics
;
Information and Systems Security
Abstract:
Perceptual image hashing has received an increased attention as one of the most important components for content based image authentication in recent years. Content based image authentication using perceptual image hashing is mainly classified into four different categories according to the feature extraction scheme. However, all the recently published literature that belongs to the individual category has its own strengths and weaknesses related to the feature extraction scheme. In this regard, this paper proposes a hybrid approach to improve the performance by combining two different categories: low-level image representation and coarse image representation. The proposed method employs a well-known local feature descriptor, the so-called Histogram of Oriented Gradients (HOG), as the feature extraction scheme in conjunction with Image Intensity Random Transformation (IIRT), Successive Mean Quantization Transform (SMQT), and bit-level permutation to construct a secure and robust hash
value. To enhance the proposed method, a Key Derivation Function (KDF) and Error Correction Code (ECC) are applied to generate a stable subkey based on the coarse image representation. The derived subkey is utilized as a random seed in IIRT and HOG feature computation. Additionally, the experimental results are presented and compared with two existing algorithms in terms of robustness, discriminability, and security.
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