Mahesh K. Singh
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Mahesh K. Singh has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Nitin Singha
Department of Electronics and Communication Engineering, National Institute of Technology Delhi, Delhi, India
3 shared publications - Priti Mehta
Institute of Pharmacy, Nirma University, Ahmedabad, India
2 shared publications - Seema Devi
Department of Electronics and Communication Engineering, National Institute of Technology Delhi, Delhi, India
1 shared publication - Swati Bhalaik
Jindal Global Business School, Sonipat, Haryana, India
1 shared publication
Latest Publications
- 1 July 2022
- Published by SPIE-Intl Soc Optical Eng in Journal of Electronic Imaging
- Vol. 31 (04) , 043043-16
Recaptured attack-resilient watermarking scheme
We propose a recaptured attack-resilient blind watermarking scheme. The work aims to recover the watermark information from the stego (watermarked image) reacquired through a digital liquid-crystal display monitor. In the age of digital globalization, problems like copyright and integrity infringement of digital content are growing exponentially. The content owner can prove their ownership by extracting the embedded watermark from the digitally breached data. The presented framework is robust to several geometrical and signal processing attacks, including lens distortions, intensity distortion, and moiré distortion caused by recapture attack. The singular values of a watermark image and a unique security key are embedded in the host image’s double-density dual-tree discrete wavelet transform (DDD-DWT) coefficients. However, most singular value decomposition (SVD) based watermarking algorithms are prone to false alarm rates and missed alarm rates, causing a security issue. On that account, the proposed watermarking scheme in this paper introduces a unique security key embedding for authentication at the decoder side. The experimental results show that the proposed framework provides high perceptibility with a WPSNR value >59 dB and robustness against various attacks and security.
- 1 July 2022
- Published by SPIE-Intl Soc Optical Eng in Journal of Electronic Imaging
- Vol. 31 (04) , 043043-16
Recaptured attack-resilient watermarking scheme
We propose a recaptured attack-resilient blind watermarking scheme. The work aims to recover the watermark information from the stego (watermarked image) reacquired through a digital liquid-crystal display monitor. In the age of digital globalization, problems like copyright and integrity infringement of digital content are growing exponentially. The content owner can prove their ownership by extracting the embedded watermark from the digitally breached data. The presented framework is robust to several geometrical and signal processing attacks, including lens distortions, intensity distortion, and moiré distortion caused by recapture attack. The singular values of a watermark image and a unique security key are embedded in the host image’s double-density dual-tree discrete wavelet transform (DDD-DWT) coefficients. However, most singular value decomposition (SVD) based watermarking algorithms are prone to false alarm rates and missed alarm rates, causing a security issue. On that account, the proposed watermarking scheme in this paper introduces a unique security key embedding for authentication at the decoder side. The experimental results show that the proposed framework provides high perceptibility with a WPSNR value >59 dB and robustness against various attacks and security.
- 1 March 2022
- Published by SPIE-Intl Soc Optical Eng in Journal of Electronic Imaging
- Vol. 31 (02) , 023017-17
Near-duplicate image detection based on wavelet decomposition with modified deep learning model
We aim to address the near-duplicate image (NDI) detection problem with a deep learning network. With the advancement of digital acquisition devices and easy-to-use image editing software, NDI forgery is ubiquitous nowadays. This rising problem demands robust detection algorithms that can efficiently prevent the NDI forgery and its distribution. We present a modified deep learning model to detect NDI forensics attacks based on extracted wavelet Haar features. Unlike standard deep learning models, a wavelet decomposed preprocessing layer is used before the deep learning networks, and a support vector machine classifier is employed in the classification stage of a modified deep learning model. The model is tested on distinctly available image databases. The experimental results show that the proposed model more effectively detect the NDIs. In addition, the comparison result shows that the modified model outperforms the other approaches significantly.
- 1 March 2022
- Published by SPIE-Intl Soc Optical Eng in Journal of Electronic Imaging
- Vol. 31 (02) , 023017-17
Near-duplicate image detection based on wavelet decomposition with modified deep learning model
We aim to address the near-duplicate image (NDI) detection problem with a deep learning network. With the advancement of digital acquisition devices and easy-to-use image editing software, NDI forgery is ubiquitous nowadays. This rising problem demands robust detection algorithms that can efficiently prevent the NDI forgery and its distribution. We present a modified deep learning model to detect NDI forensics attacks based on extracted wavelet Haar features. Unlike standard deep learning models, a wavelet decomposed preprocessing layer is used before the deep learning networks, and a support vector machine classifier is employed in the classification stage of a modified deep learning model. The model is tested on distinctly available image databases. The experimental results show that the proposed model more effectively detect the NDIs. In addition, the comparison result shows that the modified model outperforms the other approaches significantly.