A dual-tamper-detection method for digital image authentication and content self-recovery

Liu, Tong; Yuan, Xiaochen

doi:10.1007/s11042-021-11179-2

Cited by 6 publications

(1 citation statement)

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“…While most of the previously mentioned techniques primarily focus on enhancing robustness and imperceptibility, they do not address the detection of unauthorized alterations in original images. In response to this, Liu and Yuan [25] put forth a dual-tamper-detection approach that both detects tampering and facilitates self-recovery. The watermark in this method is composed of two check bits and one recovery bit.…”

Section: Introductionmentioning

confidence: 99%

A Robust Algorithm for Digital Image Copyright Protection and Tampering Detection: Employing DWT, DCT, and Blowfish Techniques

Salama,

Shoitan,

Abdallah

et al. 2023

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With the rapid proliferation of digital images on the internet, the task of preserving image ownership and ensuring the detection of unauthorized alterations has become increasingly challenging. This study introduces a robust algorithm, leveraging Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Blowfish encryption techniques, designed to maintain copyright integrity and detect image tampering. The proposed algorithm operates on a given RGB host image, first isolating it into its constituent red, green, and blue components. For the purpose of copyright protection, the algorithm applies DWT and DCT to the green component, embedding a watermark logo within it. The blue component is subjected to Blowfish encryption, generating a ciphered blue component that aids in tampering detection. Subsequently, the least significant bits of this ciphered blue component are interchanged with those of the host image's red component, producing a novel red component. This process results in the creation of a watermarked green component, an original blue component, and a newly formed red component. These are then amalgamated to produce the final watermarked image. The proposed method is evaluated using five standard images, with simulation results demonstrating its resilience to various attacks. Importantly, the algorithm exhibits a capacity to detect any unauthorized modifications up to a granularity of 2×2 pixels.

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Section: Introductionmentioning

confidence: 99%