Introduction to the special issue on deep learning for real-time information hiding and forensics

Zhou, Zhili; Yang, Ching-Nung; Kim, Cheonshik; Cimato, Stelvio

doi:10.1007/s11554-020-00947-2

Cited by 4 publications

(7 citation statements)

References 20 publications

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“…The obtained experimental results are compared with the other state-of-the-art methods [29,30,21] of image-based secret information hiding. Zhou et al utilized two formal approaches, one is information hiding and the other is digital forensics; the feature of this research was successfully applied for the prevention of multimedia copyright, privacy, security, and protection [29]. But the massive growth of data in the cloud environment creates challenges for forensic experts in this research [29,21].…”

Section: Resultsmentioning

confidence: 99%

“…Zhou et al utilized two formal approaches, one is information hiding and the other is digital forensics; the feature of this research was successfully applied for the prevention of multimedia copyright, privacy, security, and protection [29]. But the massive growth of data in the cloud environment creates challenges for forensic experts in this research [29,21]. With the rapid use of multimedia systems, in the proposed scheme we overcome threats involved in cyberenvironment, manipulating critical information such as transaction details, intellectual rights, and encrypted secrecy integrity, by using stream cipher strategy.…”

Section: Resultsmentioning

confidence: 99%

“…The obtained experimental results are compared with the other state‐of‐the‐art methods [29, 30, 21] of image‐based secret information hiding. Zhou et al.…”

Section: Resultsmentioning

confidence: 99%

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IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

Khan

Shaikh

Cheikhrouhou

et al. 2021

IET Image Processing

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Information hiding aims to embed a crucial amount of confidential data records into the multimedia, such as text, audio, static and dynamic image, and video. Image-based information hiding has been a significantly important topic for digital forensics. Here, active image deep steganographic approaches have come forward for hiding data. The least significant bit (LSB) steganography approach is proposed to conceal a secret message into the original image. First, the lightweight stream encryption cryptography encrypts secret information in the cover image to protect embedded information from source to destination. Whereas the encrypted embedded cover information into the carrier of stego-image with the help of the LSB and then transmit. In the proposed investigational scheme, a convolutional neural net is used. A model is trained to detect and extract patterns of image hidden features, encrypted stego-image optimization, and classify original and cover images of steganography. Through the experiment result on the forensic image database for mobile steganography of the Center for Statistics and Application in Forensic Evidence, the overall embedded and extracting that the proposed scheme can achieve information hiding as well as revealing with an accuracy rate of 95.1%. The experimental result shows the robustness of the model in terms of efficiency as compared to other state-of-the-art schemes.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

Khan

Shaikh

Cheikhrouhou

et al. 2021

IET Image Processing

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“…We utilise the encoder module E (•) to encode the cover image I cover and secret information M in to generate a steganographic image I encode . I cover and I encode all select RGB images whose the size is C × H × W , and the hidden message M in is a randomly generated binary message with a size of D × H × W , where D is the relative payload depth, D ∈ [1,6]. Similarly, the decoder module D(•) is utilised to accurately extract the embedded information M out from the generated image I encode .…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…In the training process of HCISNet, we randomly generate uniformly distributed binary data as secret message, which can greatly improve the generalization ability of the network. In detail, the data size is D×H×W, D∈ [1,6].…”

Section: Settingsmentioning

confidence: 99%

HCISNet: Higher‐capacity invisible image steganographic network

Liu

et al. 2021

IET Image Processing

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Information hiding as a crucial method for multimedia security and privacy protection, has received a substantial amount of attention. However, the most existing methods focus on the resistance of steganalysis and the robustness of information, resulting in low capacity. In this paper, to further increase the capacity of information hiding, a high-capacity adversarial image steganography model with end-to-end manner, termed as HCISNet is proposed. First, an enhanced Dense Atrous Spatial Pyramid Pooling module is presented to learn more semantic information in cover image, which can adaptively embed more message in redundant regions with rich textures. Then, a modified discriminator network with lightweight residual block is employed to assist the encoder network with optimal highcapacity solution. Meanwhile, the multiple objective function with the perceptual loss and several training tricks are developed, which can improve the visual and perceptual consistency of the original and the steganographic image, further enhancing the capacity. Finally, experiments and analysis are conducted on three public datasets, where this model has better imperceptibility, higher security and greater capacity. Under same experimental conditions, the cover image can embed 5.68 bits per pixel (BPP), far exceeding the previous highest value of 4.4 BPP in state-of-the-art methods in the authors' knowledge.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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