An effective digital audio watermarking using a deep convolutional neural network with a search location optimization algorithm for improvement in Robustness and Imperceptibility

Watermarking is the advanced technology utilized to secure digital data by integrating ownership or copyright protection.Most of the traditional extracting processes in audio watermarking have some restrictions due to low reliability to various attacks.Hence,a deep learning-based audio watermarking system is proposed in this research to overcome the restriction in the traditional methods.The implication of the research relies on enhancing the performance of the watermarking system using the Discrete Wavelet Transform(DWT)and the optimized deep learning technique.The selection of optimal embedding location is the research contribution that is carried out by the deep convolutional neural network(DCNN).The hyperparameter tuning is performed by the so-called search location optimization,which minimizes the errors in the classifier.The experimental result reveals that the proposed digital audio watermarking system provides better robustness and performance in terms of Bit Error Rate(BER),Mean Square Error(MSE),and Signal-to-noise ratio.The BER,MSE,and SNR of the proposed audio watermarking model without the noise are 0.082,0.099,and 45.363 respectively,which is found to be better performance than the existing watermarking models.

Randomization Strategies in Image Steganography Techniques:A Review

Image steganography is one of the prominent technologies in data hiding standards.Steganographic system performance mostly depends on the embedding strategy.Its goal is to embed strictly confidential information into images without causing perceptible changes in the original image.The randomization strategies in data embedding techniques may utilize random domains,pixels,or region-of-interest for concealing secrets into a cover image,preventing information from being discovered by an attacker.The implementation of an appropriate embedding technique can achieve a fair balance between embedding capability and stego image imperceptibility,but it is challenging.A systematic approach is used with a standard methodology to carry out this study.This review concentrates on the critical examination of several embedding strategies,incorporating experimental results with state-of-the-art methods emphasizing the robustness,security,payload capacity,and visual quality metrics of the stego images.The fundamental ideas of steganography are presented in this work,along with a unique viewpoint that sets it apart from previous works by highlighting research gaps,important problems,and difficulties.Additionally,it offers a discussion of suggested directions for future study to advance and investigate uncharted territory in image steganography.

A Robust Data Hiding Reversible Technique for Improving the Security in e-Health Care System

The authenticity and integrity of healthcare is the primary objective.Numerous reversible watermarking schemes have been developed to improve the primary objective but increasing the quantity of embedding data leads to covering image distortion and visual quality resulting in data security detection.A trade-off between robustness,imperceptibility,and embedded capacity is difficult to achieve with current algorithms due to limitations in their ability.Keeping this purpose insight,an improved reversibility watermarking methodology is proposed to maximize data embedding capacity and imperceptibility while maintaining data security as a primary concern.A key is generated by a random path with minimum bit flipping is selected in the 4 × 4 block to gain access to the data embedding patterns.The random path's complex structure ensures data security.Data of various sizes(8 KB,16 KB,32 KB)are used to analyze image imperceptibility and evaluate quality factors.The proposed reversible watermarking methodology performance is tested under standard structures PSNR,SSIM,and MSE.The results revealed that the MRI watermarked images are imperceptible,like the cover image when LSB is 3 bits plane.Our proposed reversible watermarking methodology outperforms other related techniques in terms of average PSNR(49.29).Experiment results show that the suggested reversible watermarking method improves data embedding capacity and imperceptibility compared to existing state-of-the-art approaches.

Payload Capacity Scheme for Quran Text Watermarking Based on Vowels with Kashida

The most sensitive Arabic text available online is the digital Holy Quran.This sacred Islamic religious book is recited by all Muslims worldwide including non-Arabs as part of their worship needs.Thus,it should be protected from any kind of tampering to keep its invaluable meaning intact.Different characteristics of Arabic letters like the vowels(),Kashida(extended letters),and other symbols in the Holy Quran must be secured from alterations.The cover text of the Quran and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio(PSNR)and Embedding Ratio(ER).A watermarking technique with enhanced attributes must,therefore,be designed for the Quran’s text using Arabic vowels with kashida.The gap addressed by this paper is to improve the security of Arabic text in the Holy Quran by using vowels with kashida.The purpose of this paper is to enhance the Quran text watermarking scheme based on a reversing technique.The methodology consists of four phases:The first phase is a pre-processing followed by the second phase-the embedding process phase—which will hide the data after the vowels.That is,if the secret bit is“1”,then the kashida is inserted;however,the kashida is not inserted if the bit is“0”.The third phase is the extraction process and the last phase is to evaluate the performance of the proposed scheme by using PSNR(for the imperceptibility)and ER(for the capacity).The experimental results show that the proposed method of imperceptibility insertion is also optimized with the help of a reversing algorithm.The proposed strategy obtains a 90.5%capacity.Furthermore,the proposed algorithm attained 66.1%which is referred to as imperceptibility.

Embedding and Extracting Digital Watermark Based on DCT Algorithm

The principle of digital watermark is the method of adding digital watermark in the frequency domain. The digital watermark hides the watermark in digital media, such as image, voice, video, etc., so as to realize the functions of copyright protection, and identity recognition. DCT for Discrete Cosine Transform is used to transform the image pixel value and the frequency domain coefficient matrix to realize the embedding and extracting of the blind watermark in the paper. After success, the image is attacked by white noise and Gaussian low-pass filtering. The result shows that the watermark signal embedded based on the DCT algorithm is relatively robust, and can effectively resist some attack methods that use signal distortion to destroy the watermark, and has good robustness and imperceptibility.

Imperceptible and Robust DWT-SVD-Based Digital Audio Watermarking Algorithm

Digital representation of multimedia is more advantageous than the analog one due to potentially improving the portability, efficiency, and accuracy of the information presented. Despite the challenge of having high hiding capacity like other media watermarking, still audios are likely candidates for data hiding due to their possible capabilities of achieving impressive robustness and protection against online music piracy and content identification. In this paper, we propose efficient audio watermarking embedding and extracting techniques, which mainly use Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD), in which a new matrix formation of details sub-bands is proposed. Additionally, massive experimental work was conducted to investigate the contributions of operating different watermark intensities and multiple levels of DWT to our proposed techniques. Two performance objectives are employed in this work which involve imperceptibility and robustness. To further boost the imperceptibility, we incorporate the code assignment method to our techniques that do outperform what are closely connected in the literature.

A User-Friendly SSVEP-Based BCI Using Imperceptible Phase-Coded Flickers at 60Hz

A brain-computer interface(BCI)system based on steady-state visual evoked potentials(SSVEP)was developed by four-class phase-coded stimuli.SSVEPs elicited by flickers at 60Hz,which is higher than the critical fusion frequency(CFF),were compared with those at 15Hz and 30Hz.SSVEP components in electroencephalogram(EEG)were detected using task related component analysis(TRCA)method.Offline analysis with 17 subjects indicated that the highest information transfer rate(ITR)was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%.The online BCI system reached an averaged classification accuracy of 87.75±3.50%at 60Hz with 4s,resulting in an ITR of 16.73±1.63bpm.In particular,the maximum ITR for a subject was 80bpm with 0.5s at 60Hz.Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz,the results of the behavioral test indicated that,with no perception of flicker,the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz.Correlation analysis revealed that SSVEP with higher signal-to-noise ratio(SNR)corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance.This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.

Local imperceptible adversarial attacks against human pose estimation networks

Deep neural networks are vulnerable to attacks from adversarial inputs.Corresponding attack research on human pose estimation(HPE),particularly for body joint detection,has been largely unexplored.Transferring classification-based attack methods to body joint regression tasks is not straightforward.Another issue is that the attack effectiveness and imperceptibility contradict each other.To solve these issues,we propose local imperceptible attacks on HPE networks.In particular,we reformulate imperceptible attacks on body joint regression into a constrained maximum allowable attack.Furthermore,we approximate the solution using iterative gradient-based strength refinement and greedy-based pixel selection.Our method crafts effective perceptual adversarial attacks that consider both human perception and attack effectiveness.We conducted a series of imperceptible attacks against state-of-the-art HPE methods,including HigherHRNet,DEKR,and ViTPose.The experimental results demonstrate that the proposed method achieves excellent imperceptibility while maintaining attack effectiveness by significantly reducing the number of perturbed pixels.Approximately 4%of the pixels can achieve sufficient attacks on HPE.