Digital Watermarking in Video for Copyright Protection

Jadhav, Anita; Kolhekar, Megha

doi:10.1109/icesc.2014.29

Cited by 13 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Video watermarking is also a promising future direction. There are existing traditional algorithms for video watermarking [40], and deep learning techniques are also being considered, for example in [92]. This paper introduces RIVAGAN, a new architecture for robust video watermarking.…”

Section: Expanding Applications For Deep Learning Digital Watermarkin...mentioning

confidence: 99%

“…The primary application of digital watermarking is for the authentication of digital assets, however, the process also has uses for licences and identification [30], digital forensics [9], and data protection in smart cities [22,58]. Digital watermarking is not only useful for marking images and documents, but also for real-time audios and videos [33,40], languages [1], as well as chip and hardware protection in electronics [63].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography

Wang¹,

Olivia²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Data hiding is the process of embedding information into a noise-tolerant signal such as a piece of audio, video, or image. Digital watermarking is a form of data hiding where identifying data is robustly embedded so that it can resist tampering and be used to identify the original owners of the media. Steganography, another form of data hiding, embeds data for the purpose of secure and secret communication. This survey summarises recent developments in deep learning techniques for data hiding for the purposes of watermarking and steganography, categorising them based on model architectures and noise injection methods. The objective functions, evaluation metrics, and datasets used for training these data hiding models are comprehensively summarised. Finally, we propose and discuss possible future directions for research into deep data hiding techniques.CCS Concepts: • Security and privacy; • Computing methodologies → Computer vision;

show abstract

Section: Expanding Applications For Deep Learning Digital Watermarkin...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography

Wang¹,

Olivia²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The other way, the common compression algorithms remove the motionless area of the video to achieve higher compression rates, therefore, the embedding of the watermark is done in the motion area to resist various attacks. The various schemes proposed in the literature are [44,[46][47][48][49][50][51][52].…”

Section: Classification Of Video Watermarking Algorithms Based On Motmentioning

confidence: 99%

Video Watermarking Techniques: A Review

Leelavathy¹,

Prasad²,

Kumar³

2014

IJCA

View full text Add to dashboard Cite

Present era shows a rapid growth in the progress of digitization and creation of digital multimedia content. Image and Video makes up the majority component in digital multimedia content. The potential solution for protection and prohibiting copyright infringement of multimedia is only using digital watermarks. The characteristic of robustness, i.e., sustainable capability to withstand against various attacks, influences the applications and its performance in protection of copyright and authentication. This paper reviews importance of watermarking, design requirements for various applications, and focus on classification of video watermarking algorithms for copyright protection.

show abstract

“…En general, los métodos propuestos cumplen con las condiciones de imperceptibilidad y robustez, pero tienen una capacidad de ocultamiento muy baja. Por ejemplo, de 45.9 bps (DWT) [4], 43 bps (dominio del tiempo) [11], 25.6 bps (DWT) [12], 27.1 bps (FFT) [13], 4.2 bps (dominio del tiempo) [14] y 2 bps (dominio del tiempo) [15].…”

Section: Introductionunclassified

Marcado de audio mejorado en el dominio wavelet

Pinto¹,

Ballesteros²,

Renza³

2018

Vis. Electron.

View full text Add to dashboard Cite

Historia del artículo:Palabras clave: Marcado de agua Procesamiento de imágenes Transformada wavelet discreta Keywords: Watermarking Image processing Discrete wavelet transform Citar este artículo como: E. Pinto, D. M. Ballesteros y D. Renza, "Marcado de audio mejorado en el dominio wavelet". Visión Electrónica, algo más que un estado sólido, Vol. 12, No. 2, 198-207, julio-diciembre 2018.. https://doi.

show abstract

Digital Watermarking in Video for Copyright Protection

Cited by 13 publications

References 2 publications

Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography

Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography

Video Watermarking Techniques: A Review

Marcado de audio mejorado en el dominio wavelet

Contact Info

Product

Resources

About