Survey on deep learning applications in digital image security

Bao, Zhenjie; Ren, Xue

doi:10.1117/1.oe.60.12.120901

Cited by 8 publications

(3 citation statements)

References 141 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, deep learning-based watermarking algorithms have gradually become popular among watermarking technology researchers [26]. Deep learning models for image processing are used in image watermarking systems [27]. Compared with traditional watermarking methods, deep learning-based algorithms can better adapt to different image contents and provide higher robustness and security [28].…”

Section: Introductionmentioning

confidence: 99%

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

et al. 2023

View full text Add to dashboard Cite

In the continuous progress of mobile internet technology, medical image processing technology is also always being upgraded and improved. In this field, digital watermarking technology is significant and provides a strong guarantee for medical image information security. This paper offers a robustness zero watermarking strategy for medical pictures based on an Improved NasNet-Mobile convolutional neural network and the discrete cosine transform (DCT) to address the lack of robustness of existing medical image watermarking algorithms. First, the structure of the pre-training network NasNet-Mobile is adjusted by using a fully connected layer with 128 output and a regression layer instead of the original Softmax layer and classification layer, thus generating a regression network with 128 output, whereby the 128 features are extracted from the medical images using the NasNet-Mobile network with migration learning. Migration learning is then performed on the modified NasNet-Mobile network to obtain the trained network, which is then used to extract medical image features, and finally the extracted image features are subjected to DCT transform to extract low frequency data, and the perceptual hashing algorithm processes the extracted data to obtain a 32-bit binary feature vector. Before performing the watermark embedding, the watermark data is encrypted using the chaos mapping algorithm to increase data security. Next, the zero watermarking technique is used to allow the algorithm to embed and extract the watermark without changing the information contained in the medical image. The experimental findings demonstrate the algorithm’s strong resistance to both conventional and geometric assaults. The algorithm offers some practical application value in the realm of medicine when compared to other approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In recent years, the rapid development of modern industrialization processes has led to a significant increase in emissions of industrial smoke and fumes, exacerbating environmental air pollution. 1 Industrial smoke has emerged as one of the major sources of atmospheric pollution, posing a serious threat to human health due to the substantial release of industrial smoke into the atmosphere. 2 Consequently, the evaluation of industrial smoke pollution levels has become an effective approach for energy conservation and mitigating environmental pollution.…”

Section: Introductionmentioning

confidence: 99%

Video segmentation of industrial smoke based on dynamic fully convolutional network-Gaussian mixture model and multi-scale fusion attention module

Wenyu,

Hui,

Fugang

2023

J. Electron. Imag.

View full text Add to dashboard Cite

“…LSB and LSB Matching (LSBM) are notable examples of this work [10]. As a result, adaptive steganography employs various techniques to hide the hidden message [11].…”

Section: Introductionmentioning

confidence: 99%

Identifying optimal message embedding location in audio steganography using generative adversarial networks

Hajer¹,

Anbar

2022

EEJET

View full text Add to dashboard Cite

Audio steganography (AS) uses the auditory redundancy of the human ear to conceal the hidden message inside the audio track. In recent studies, deep learning-based steganalysis has swiftly revealed AS by extracting high-dimensional stego acoustic features for categorization. There is still an opportunity for improvement in the current audio steganography required for managing communication confidentiality, access control and data protection. The main objective of this research is to improving the data protection by identifying the data embedding location in the audio. Generative Adversarial Network-based Audio Steganography Framework (GAN-ASF) is presented in this study, and it can automatically learn to provide better cover audio for message embedding. The suggested framework's training architecture comprises a generator, a discriminator, and a steganalyzer learned using deep learning. The Least Significant Bit Matching (LSBM) message embedding technique encrypts the secret message into the steganographic cover audio, which is then forwarded to a trained steganalyzer for misinterpretation as cover audio. After performing the training, stenographic cover audio has been generated for encoding the secret message. Here, Markov model of co-frequency sub images to generate the best cover frequency sub-image to locate an image's hidden payload. Steganographic cover audio created by GAN-ASF has been tested and found to be of excellent quality for embedding messages. The suggested method's detection accuracy is lower than that of the most current state-of-the-art deep learning-based steganalysis. This payload placement approach has considerably increased stego locations' accuracy in low frequencies. The test results GAN-ASF achieves a performance ratio of 94.5 %, accuracy ratio of 96.2 %, an error rate of 15.7 %, SNR 24.3 %, and an efficiency ratio of 94.8 % compared to other methods.

show abstract

Survey on deep learning applications in digital image security

Cited by 8 publications

References 141 publications

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

Video segmentation of industrial smoke based on dynamic fully convolutional network-Gaussian mixture model and multi-scale fusion attention module

Identifying optimal message embedding location in audio steganography using generative adversarial networks

Contact Info

Product

Resources

About