A hybrid digital image watermarking technique based on fuzzy-BPNN and shark smell optimization

Rai, Manish; Goyal, Sachin

doi:10.1007/s11042-022-12712-7

Cited by 12 publications

(6 citation statements)

References 20 publications

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“…However, due to the training overfitting of PSO [95], its model generalized and achieved good robust performance only on the experimental dataset. Rai and Goyal [96] combined fuzzy, backpropagation neural networks and shark optimization algorithms. However, refs.…”

Section: Gan-basedmentioning

confidence: 99%

“…However, refs. [94,96] had the problem of training overfitting. To improve the training overfitting problem, Liu et al [97] introduced a two-dimensional image information entropy loss to enhance the ability of the model to generate different watermarks, ensuring that the model was always able to assign enough information to a single host image for different watermark inputs and the extractor can extract the watermark information completely, therefore enhancing the dynamic randomness of the watermark embedding.…”

Section: Gan-basedmentioning

confidence: 99%

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Review of Image Forensic Techniques Based on Deep Learning

et al. 2023

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Digital images have become an important carrier for people to access information in the information age. However, with the development of this technology, digital images have become vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order, and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics, and commonly used datasets for forgery detection are presented. The performance, advantages, and disadvantages of existing methods are also compared and analyzed according to the different types of detection. In active forensics, robust image watermarking techniques are overviewed, and the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are presented to provide useful suggestions for people in image forensics and related research fields.

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Section: Gan-basedmentioning

confidence: 99%

Section: Gan-basedmentioning

confidence: 99%

Review of Image Forensic Techniques Based on Deep Learning

et al. 2023

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“…Mun [89] Fan [81] Kang [90] Rai [92] Overfitting Liu [93] Zhao [94] Improve overfitting Geometric Zero Watermarking Transform Domain-based Ahmadi [100] Mei [101] Han [102] Liu [104] Lack combined attacks robustness Gong [ Noise and filtering attacks apply corresponding noise or filtering to the pixel from the spatial domain and transform domain directly, affecting the amplitude coefficient synchronization of the codec. There are three methods for recovering amplitude synchronization: zero-watermarking, generative adversarial network (GAN) [80]-based, and embedding coefficient optimization.…”

Section: Embedding Parameter Optimizationmentioning

confidence: 99%

“…However, due to the training overfitting of PSO [91], its model generalized and achieved good robust performance only on the experimental dataset badly. Rai and Goyal [92] combined fuzzy, backpropagation neural networks and shark optimization algorithms. However, [90,92] had the problem of training overfitting.…”

Section: Embedding Parameter Optimizationmentioning

confidence: 99%

“…Rai and Goyal [92] combined fuzzy, backpropagation neural networks and shark optimization algorithms. However, [90,92] had the problem of training overfitting. To improve the training overfitting problem, Liu et al [93] introduced a two-dimensional image information entropy loss to enhance the ability of the model to generate different watermarks, ensuring that the model was always able to assign enough information to a single host image for different watermark inputs and the extractor can extract the watermark information completely, therefore enhancing the dynamic randomness of the watermark embedding.…”

Section: Embedding Parameter Optimizationmentioning

confidence: 99%

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Review on Image Forensic Techniques Based on Deep Learning

Shi¹,

Chen²,

Wang³

et al. 2023

Preprint

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Digital images have become an important carrier for people to access information in the information age. However, with the development of the technology, digital images are vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics and commonly used datasets for forgery detection are presented. The performance, advantages and disadvantages of existing methods are also compared and analyzed according to different types of detection. In active forensics, robust image watermarking techniques are overviewed, the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are given to provide useful suggestions for people in image forensics and related research fields.

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