Water chaotic fruit fly optimization-based deep convolutional neural network for image watermarking using wavelet transform

Ingaleshwar, Subodh; Dharwadkar, Nagaraj V.; Jayadevappa, D.

doi:10.1007/s11042-020-10498-0

Cited by 21 publications

(9 citation statements)

References 23 publications

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“…Despite the good number of existing techniques of deep learning-based watermarking proposed for images, video watermarking based on deep learning has only recently begun to be explored and is still an open problem. In fact, as far as we know, there are only a very few number of video watermarking techniques based on deep learning in the literature [16,26,35,40,43,56,59,65,91,97] that appeared since 2019.…”

Section: Deep Learning-based Video Watermarking Reviewmentioning

confidence: 99%

“…Deep learning-based video watermarking techniques can be classified based on the original video domain which can be the original frames or the compressed domain. In fact, [16,26,35,40,43,56,59,65,91,97] have been proposed for video frames where [16,35,43,56,65,91,97] are proposed for robust multi-bit embedding. [26] is a robust zero watermarking and [40] is proposed recently for compressed videos.…”

Section: Classification Of Deep Learning-based Video Watermarking Tec...mentioning

confidence: 99%

“…Reversible medical video watermarking with a Deep CNN based on SCBSA is discussed in [35]. In order to integrate videos, the approach combines the Sine Cosine and Bird Swarm Algorithm (SCBSA), which includes key frame extraction, region identification, and embedding.…”

Section: Classification Of Deep Learning-based Video Watermarking Tec...mentioning

confidence: 99%

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Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends

Ben Jabra,

Ben Farah

2024

Circuits Syst Signal Process

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The digital revolution places great emphasis on digital media watermarking due to the increased vulnerability of multimedia content to unauthorized alterations. Recently, in the digital boom in the technology of hiding data, research has been tending to perform watermarking with numerous architectures of deep learning, which has explored a variety of problems since its inception. Several watermarking approaches based on deep learning have been proposed, and they have proven their efficiency compared to traditional methods. This paper summarizes recent developments in conventional and deep learning image and video watermarking techniques. It shows that although there are many conventional techniques focused on video watermarking, there are yet to be any deep learning models focusing on this area; however, for image watermarking, different deep learning-based techniques where efficiency in invisibility and robustness depends on the used network architecture are observed. This study has been concluded by discussing possible research directions in deep learning-based video watermarking.

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Section: Deep Learning-based Video Watermarking Reviewmentioning

confidence: 99%

Section: Classification Of Deep Learning-based Video Watermarking Tec...mentioning

confidence: 99%

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Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends

Ben Jabra,

Ben Farah

2024

Circuits Syst Signal Process

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“…Though it has many applications, CNN is used primarily in watermarking for embedding the watermark, extracting the watermark, increasing the visual quality of the cover image, and identifying the best embedding location for the watermark. Ingaleshwar and Dharwadkar 18 proposed an optimization-based watermarking technique using deep CNN to embed the watermark image into the cover image. Decomposition on the cover image was performed into the different grids to obtain the related features of gridlines.…”

Section: State-of-the-art Watermarking In Deep-learning Environmentsmentioning

confidence: 99%

Comprehensive review of watermarking techniques in deep-learning environments

Singh

2022

J. Electron. Imag.

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Recently, the demand for the generation, sharing, and storage of massive amounts of multimedia information-especially in the form of images-from different intelligent devices and sensors has increased drastically. This introduces issues including the illegal access and fraudulent usage of this information as well as other security concerns. Watermarking consists of embedding a watermark design in a digital cover and then later extracting it to provide a solution for ownership conflict and copyright violation issues involving the media data. Presently, in watermarking, the use of deep-learning approaches is incredibly beneficial due to their accuracy, superior results and strong learning ability. We present a comprehensive review of watermarking techniques in deep-learning environments. We start with basic concepts of traditional and learning-based digital watermarking; we later review the popular deep-learning model-based digital watermarking methods; then, we summarize and compare the most recent contribution in the literature; finally, we highlight obfuscation challenges and further research directions.

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“…Sayısal görüntü için oluşturulan vektör listesi Karınca Kolonisi Optimizasyonu (KKO) metodu ile diğer algoritmalara göre çok daha kısa sürede elde edilmiştir [20]. Parametre sayısı ve uygulama kolaylığı nedeniyle MSO metodu da analitik çözümü olmayan mühendislik problemlere başarılı yaklaşık çözümler üretebilmiştir [21][22][23][24][25][26][27][28][29][30][31][32], [37][38][39][40]. En son çıkan tekniklerden biri olan Yarasa Optimizasyonu (YO) birçok tekniğin birleştirilmesi ile literatürde yerini almıştır [33].…”

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Levy Uçuşlu Meyve Si̇neği̇ Algori̇tmasi İle Görüntü Sikiştirma

Kılıç

2022

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

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Sayısal görüntülerin sıkıştırılıp arşivlenmesi günümüz teknolojisinde çok önemli bir ihtiyaç haline gelmiştir. Son yıllarda doğadan esinlenerek geliştirilen PSO(Parçacık sürü optimizasyonu), MSO(Meyve sineği optimizasyonu), ABO(Ateşböceği optimizasyonu), GA(Genetik Algoritma) gibi sezgisel metodlar da vektör tabanlı görüntü sıkıştırma için kullanılmaya başlamıştır. Bu çalışmada MSO, meyve sineklerinin sorunsuz bir şekilde global optimum noktaya ulaşabilmesi için Levy Uçuşu tekniği ile birleştirilmiştir. MSO algoritmasının en büyük sorunlarından biri de lokal minimum noktaya takılıp global minimuma ulaşamamasıdır. Çoğu zaman küçük nadiren de büyük yarıçap değeri veren Levy Fonksiyonu yardımı ile meyve sineği lokal minimum noktaya hiç takılmayıp global minimum noktayı garantilemektedir. Bu yeni geliştirilen LMSO(Levy uçuşlu meyve sineği optimizasyonu) tekniği standart görüntüler üzerinde test edilmiş ve aynı sıkıştırma oranlarında MSE, PSNR ölçütleri kullanıldığında diğer sezgisel algoritmalardan üstün olduğu gösterilmiştir.

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Water chaotic fruit fly optimization-based deep convolutional neural network for image watermarking using wavelet transform

Cited by 21 publications

References 23 publications

Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends

Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends

Comprehensive review of watermarking techniques in deep-learning environments

Levy Uçuşlu Meyve Si̇neği̇ Algori̇tmasi İle Görüntü Sikiştirma

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