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Amir Hossein Taherinia
Researcher at Ferdowsi University of Mashhad
Publications - 43
Citations - 305
Amir Hossein Taherinia is an academic researcher from Ferdowsi University of Mashhad. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 8, co-authored 36 publications receiving 202 citations. Previous affiliations of Amir Hossein Taherinia include Sharif University of Technology.
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TRLH: Fragile and blind dual watermarking for image tamper detection and self-recovery based on lifting wavelet transform and halftoning technique
TL;DR: This method generates two image digests from the host image, based on the lifting wavelet and the halftoning technique, which shows the efficiency of TRLH compared to the state of the art methods.
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TRLG: Fragile blind quad watermarking for image tamper detection and recovery by providing compact digests with optimized quality using LWT and GA
TL;DR: An efficient fragile blind quad watermarking scheme, named TRLG, is proposed for image tamper detection and recovery based on lifting wavelet transform and genetic algorithm, with superiority in quality of the watermarked and recovered images, tamper localization, and security compared with the state-of-the-art methods.
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An Effective Semi-fragile Watermarking Method for Image Authentication Based on Lifting Wavelet Transform and Feed-Forward Neural Network
TL;DR: The proposed method is superior in terms of robustness and quality of the watermarked and recovered images, respectively, compared to the state-of-the-art methods, and imperceptibility has been improved by using different correlation steps as the gain factor for flat and texture blocks.
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High capacity image steganography on sparse message of scanned document image (SMSDI)
TL;DR: This paper presents a novel algorithm for high capacity image steganography, whose aim is to hide a scanned document as a message into a host image using halftoning algorithm to convert the gray-scale scanned document into a binary image, which is a sparse matrix.
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WACA: a new blind robust watermarking method based on Arnold Cat map and amplified pseudo-noise strings with weak correlation
TL;DR: A robust and blind watermarking method is proposed, which is highly resistant to the common imageWatermarking attacks, such as noises, compression, and image quality enhancement processing.