Discrete wavelet transform based video watermarking technique

Kadu, Sneha; Naveen, Ch.; Satpute, V. R.; Keskar, Avinash G.

doi:10.1109/microcom.2016.7522422

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…Figures 15 and 17 show corresponding extracted watermark with BER of 0.005% and 0.03%, respectively. Table 1 shows BER performance between different methods (Sake and Tirumala, 2016;Shukla and Sharma, 2018;Kadu et al, 2016) with different attacks. We implemented methods as given in the paper.…”

Section: Resultsmentioning

confidence: 99%

“…So, we fixed  to 0.95. We also tried compression methods like avi, mkv jpeg and we achieved good performance and bit error rates are better than mp4 compression BDWTSVD (Sake and Tirumala, 2016) Level-3 DWT (Shukla and Sharma, 2018) DWT (Kadu et al, 2016 Table 3 shows BER variation against different attacks by changing different kinds of embedding equation ( 5).…”

Section: Resultsmentioning

confidence: 99%

“…As the method inserts watermark bit into multiple frames, retrieving of watermark bit becomes challenging when frame drop attack is presented and also finding the starting frame out of 32 frames during extraction process is very difficult when the clipping attack is applied. Kadu et al (2016) employed key-based watermarking insertion along with DWT, where key is generated using watermark image and low frequency coefficients in DWT domain. These keys are also required during extraction of watermark bit though original video is not required.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Video watermarking using neural networks

Latha¹,

Reddy²,

Damodaram³

2021

IJICS

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Copyright protection for videos is important to prevent revenue loss for video generation companies by using video watermarking methods. Though many methods exists, still certain scope is noticed in robust video watermarking methods. Achieving features like trade-off between robustness and imperceptibility, speed, blind watermarking simultaneously is very challenging. The proposed work achieves the above said features using log-polar, DWT, and SVD techniques to embed watermark in a video and extract it when necessary. The objective is to protect the copyright and make the watermarking system blind, robust against frame drop attacks as well as achieving above features. This work also leverages scrambling, deep learningbased approach to generate secret sharing image from watermark to improve the speed compared to conventional tabular-based approach. We evaluated the method on our own dataset and proved that this method is outperforming compared to state-of-the-art methods in DWT and SVD domain.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Video watermarking using neural networks

Latha¹,

Reddy²,

Damodaram³

2021

IJICS

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“…Author also used the chrominance channel to improve the quality of watermarking method. Kadu et al [18,19] has transformed the spatial domain to frequency domain using DWT to achieve the effective copyright protection. Author used the low-frequency part (LL) sub-band as the cover to hide a watermark image within videos.…”

Section: Related Workmentioning

confidence: 99%

Frame Selective and Dynamic Pattern Based Model for Effective and Secure Video Watermarking

Juneja¹,

Bansal²

2019

IJC

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Video watermarking enables the users to share the digital contents in public domain without any issue. However, video watermarking is also infected by various attacks that can even destroy the hidden watermark. The security, robustness and reliability are the key challenges of any Video watermarking technique. In this paper, such a robust and dynamic video watermarking framework is presented that can handle most of the existing issues. In this framework, the video is hiding on the selective frames and the preference of frames is done based on structural feature evaluation. After performing the frame selection, the featured analysis is accomplished on each frame to identify the effective cover blocks. The structure feature analysis based rules are applied to identify the most secure region over the frame. In the final stage, the histogram shift method is applied to perform data hiding. To improve the security, instead of hiding the complete image, the ECC (Elliptic Curve Cryptography) encrypted image is watermarked within the video frames. The analysis is performed on real time videos. The analysis results are generated in terms of MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity Measure) and BCR (Bit Correct Ratio) parameters. The results identified that the proposed model has improved the effectiveness, robustness and security against various attacks. The comparative results are also validated against the DCT and DCT-SVD based video watermarking methods.

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“…Sundararajan [15] inserted watermark in a video by partitioning it into number of frames, then watermark image was sliced into bit planes and permuted them in order to embed into the segmented shot, but this method fail against crop attack. Method in [16] exploited discrete wavelet transform to generate a key with the help of watermark and binarized low frequency part of the video frame and same is applied on every frame to extract a corresponding key. These keys were used during extraction process.…”

Section: Related Workmentioning

confidence: 99%

Robust Video Watermarking Using Secret Sharing, SVD, DWT and Chaotic Firefly Algorithm

Bhargavi¹,

Dasari²,

Damodaram³

2018

IJIES

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Abstract:Watermarking is the process to protect and discourage the illegal sharing of multimedia content through digital data sharing websites over INTERNET and also to prove ownership of the multimedia content. Though various watermarking solutions are available to safeguard the digital media, at the same time many methods exist to weaken the strength of watermark hence ownership is disproved and can be shared illegally. Strength of watermark can be increased to make it to robust against these methods at the cost of perceptual quality. So, a compromised approach is required. This paper proposes a video watermarking solution to maintain robustness as well perceptual quality through optimization. This method adds secretly shared watermark bits to singular values of the discrete wavelet coefficients with proper scaling factor, which is selected by using the Chaotic Firefly Algorithm optimization method. Despite many watermarking methods exist on combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD), these are not effective against all filtering attacks as these are not using all frequency coefficients either for embedding or secret sharing. This method generates secretly shared watermark based on singular values in DWT domain to make the system more robust against filtering attacks and video compression techniques. This solution leverages chaotic firefly algorithm to compromise robustness and perceptual quality of the input along with SVD and DWT. The performance of method is measured by evaluating it on a dataset comprised of 140 videos of various genre and compared its performance with state of art methods. Eventually, proved the method is performing well when compared to state of art methods with respect to robustness as well as quality at the cost of computation. The stated results in experimental section shows us that watermark can be retrieved even if watermarked audio undergoes several attacks like compression at lower bit rates, frame drop attack and resize attack etc.

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Discrete wavelet transform based video watermarking technique

Cited by 10 publications

References 8 publications

Video watermarking using neural networks

Video watermarking using neural networks

Frame Selective and Dynamic Pattern Based Model for Effective and Secure Video Watermarking

Robust Video Watermarking Using Secret Sharing, SVD, DWT and Chaotic Firefly Algorithm

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