Robust image hashing based on structural and perceptual features for authentication of color images

Abstract

Image hashing is one of the most celebrated techniques regarding the discipline of image forensics, image retrieval, image indexing, content verification, and zero watermarking. For such sensitive and complex problems, the generation of unique and robust image hash is an utmost prerequisite for an image identifier driven from the perceptual contents of an image. As a design perspective, it is essential for an image hash to have robustness and optimized discriminative capability. We propose a robust image hashing technique by acquiring perceptual features based on a novel distance magnitude profile utilizing color pixel incongruity among the contiguous pixels, as well as producing a structural image for discrimination. The combination of both results provide state-of-the-art robust profile that has efficient discriminative capacity and is impervious to usual signal processing violations.

On-the-Fly Parallel Processing IP-Core for Image Blur Detection, Compression, and Chaotic Encryption Based on FPGA

“…The PRNGs are based on deterministic functions that are implemented in the digital domain using the computers or the Field Programmable Gate Arrays (FPGAs). In the previous years, the chaos theory has played an important role in the design of the PRNGs, image hashing [60], and modulation schemes [61]. The Rössler [6] and Lorenz [7] are two of the most famous chaotic systems, and they have been utilized in a lot of encryption applications [8,9].…”

Automatic Extraction of Color Features from Landscape Images Based on Image Processing

“…The existing studies mostly qualify the application effect of landscape colors [22][23][24][25][26][27][28][29], but rarely quantify the effect through image colorization. Besides, it is unreasonable to analyze landscape images with multiple standard colors with a single color space.…”