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Towards Secured Image Steganography Based on Content-adaptive Adversarial Perturbation by Vipal Kumar Sharma; Roohie Naaz Mir; Ranjeet Kumar Rout is a Computer Science article available to read on EtoBox.

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In this paper, a content-adaptive adversarial steganography is proposed to improve steganography security by adaptively adding perturbations to cover images while considering image contents with rich texture, where perturbations are generated using adversarial example generation methods like the fast gradient sign method. In this approach, a hybrid texture descriptor is initially created to describe texture regions by using better local binary patterns based on multi-grained gradient information and the noise residual feature to describe texture regions. The input image is then divided into different sections using local semantics using a segmentation approach called simple linear iterative clustering. Finally, using the hybrid texture descriptor and segmentation results, a weighted mask is created, which may be used to determine the best spots for applying adversarial perturbations of various weights to generate more secure adversarial cover images. Extensive experiments are carried out to compare the suggested method to existing state-of-the-art methods in order to prove its superiority. The experiments were conducted on BOSSbase ver. 1.01, which contains 10,000 grayscale 512*512

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Author

Vipal Kumar Sharma; Roohie Naaz Mir; Ranjeet Kumar Rout

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)