An adaptive HVS based video watermarking scheme for multiple watermarks using BAM neural networks and fuzzy inference system

Agilandeeswari, L.; Kaliyaperumal, Ganesan

doi:10.1016/j.eswa.2016.05.019

Cited by 41 publications

(16 citation statements)

References 27 publications

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“…This method provides better results, but involves expensive computation. Moreover, Loganathan and Kaliyaperumal [31] have elucidated an effectual rescindable adaptive video watermarking system for multiple watermarks on the basis of bi‐directional associative memory (BAM) neural networks and fuzzy inference system (multi‐BAM‐FUZ) scheme. This adaptive embedding strength was engendered with the help of the fuzzy inference system that receipts HVS features namely luminance, texture, and edge of each frame as an input in the DWT transform.…”

Section: Related Workmentioning

confidence: 99%

Biometric‐based efficient medical image watermarking in E‐healthcare application

Aparna

Kishore

2019

IET Image Processing

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Section: Related Workmentioning

confidence: 99%

Biometric‐based efficient medical image watermarking in E‐healthcare application

Aparna

Kishore

2019

IET Image Processing

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“…Step 7: The embedded watermark is embedded from the diagonal matrix of 2 whl s as shown in Eq. (19).…”

Section: E Watermark Extraction Processmentioning

confidence: 99%

“…Furthermore, robustness can be enhanced with the transform domain video watermarking, in which the video signal is transformed into a lowfrequency domain. Thus, video watermarking remains a prospective scheme for protecting copyrights [18,19] .…”

Section: Introductionmentioning

confidence: 99%

Video Watermarking for Copyright Protection with Genetic-Based Frame Selection

Alotaibi¹

2019

JKAU Comp IT Sci

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Recently, video watermarking has received much consideration. Several applications in a variety of domains have been implemented, and many are progressing. This paper intends to formulate a novel video watermarking framework that includes three stages: (i) Optimal video frame prediction, (ii) A watermark embedding process, and (iii) A watermark extraction process. In the proposed model, the optimal frame prediction is carried out using the deep belief network (DBN) framework. Initially, randomly chosen frames from each video are used as the input to a genetic algorithm (GA) model that optimally chooses the frames such that the peak signal-tonoise ratio (PSNR) should be maximal. The frames are assigned with a label of one or zero, where a label of one denotes a frame with better PSNR (can select for embedding process) and a label of zero denotes the frame with reduced PSNR (cannot be used for embedding). Consequently, a data library is formed from the obtained results, where each video frame is determined with their graylevel cooccurrence matrix (GLCM) features and labels (can embed or not), which is then trained in the DBN framework, from which the optimal frames can be predicted efficiently while testing. Furthermore, the watermark embedding process and watermark extraction process are carried out, and thus, the image can be embedded within the optimally selected frames.

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“…Swarm intelligence inspired by the biological flying kits and nature inspired function in artificial intelligence. The nature of swarm intelligence is meta-heuristic and diverse solution of define problem [33][34][35][36]. In this section describe the two swarm-based algorithms such as PSO and ACO algorithm.…”

Section: Swarm Intelligence Algorithmsmentioning

confidence: 99%

Optimized Features of SIFT Transform Function for Digital Image Watermarking using Hybrid Swarm Intelligence and Neural Network

Kumar¹,

Sharma²

2019

IJRTE

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Authentication of digital multi-media is a challenging task in the current scenario of internet technology for the authentication of digital multi-media data used digital watermarking techniques. The feature-based watermarking techniques provide the robustness of digital watermarking methods. In this paper proposed optimized features based digital watermarking techniques using a neural network. For the process of features, optimization used hybrid swarm intelligence algorithms. The hybrid algorithms are a combination of PSO and ACO. The PSO used as feature optimizer and ACO used for the selection of features point for the processing of neural network models. Two neural networks models used BP and RBF. The property of both models is different for the processing of data and desired output for the enhancement of the proposed model used cascaded neural network models using BP and RBF. The cascaded models generate dynamic patterns for the embedding of a digital watermark. The dynamic patterns provide the randomness of the pixel value and decrease the value of attack predication. The proposed algorithms have been tested on an extensive database of 300 images. The analysis of the proposed algorithm is satisfactory against different types of attacks and enhance the strength of robustness.

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An adaptive HVS based video watermarking scheme for multiple watermarks using BAM neural networks and fuzzy inference system

Cited by 41 publications

References 27 publications

Biometric‐based efficient medical image watermarking in E‐healthcare application

Biometric‐based efficient medical image watermarking in E‐healthcare application

Video Watermarking for Copyright Protection with Genetic-Based Frame Selection

Optimized Features of SIFT Transform Function for Digital Image Watermarking using Hybrid Swarm Intelligence and Neural Network

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