A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis

doi:10.3837/tiis.2020.03.017

Cited by 23 publications

(3 citation statements)

References 62 publications

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“…Since this is a relatively new area of research, current surveys on data hiding primarily concentrate on traditional algorithms. There are existing works examining deep learning-based techniques for steganography and cryptography [38,45], but there is a lack of works examining deep watermarking techniques. There is an existing survey looking at deep learning-based watermarking and steganography [89]; however, a comprehensive survey regarding deep data hiding models unifying digital watermarking and steganography is still lacking.…”

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Olivia²,

Wang³

et al. 2021

Preprint

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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;

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Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Olivia²,

Wang³

et al. 2021

Preprint

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“…Therefore, given this prevalent electronic warfare on open networks, securing and protecting the information exchanged has become a crucial priority. Organizations and individuals are therefore resorting to relying on techniques such as encryption and information concealment algorithms to overcome security problems [1].…”

Section: Introductionmentioning

confidence: 99%

CNN Auto-Encoder Network Using Dilated Inception for Image Steganography

Kich¹,

Ameur²,

Taouil³

2021

IJFIS

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Numerous studies have used convolutional neural networks (CNNs) in the field of information concealment as well as steganalysis, achieving promising results in terms of capacity and invisibility. In this study, we propose a CNN-based steganographic model to hide a color image within another color image. The proposed model consists of two sub-networks: the hiding network is used by the sender to conceal the secret image; and the reveal network is used by the recipient to extract the secret image from the stego image. The architecture of the concealment sub-network is inspired by the U-Net auto-encoder and benefits from the advantages of the dilated convolution. The reveal sub-network is inspired by the auto-encoder architecture. To ensure the integrity of the hidden secret image, the model is trained end to end: rather than training separately, the two sub-networks are trained simultaneously a pair of networks. The loss function is elaborated in such a way that it favors the quality of the stego image over the secret image as the stego image is the one that comes under steganalysis attacks. To validate the proposed model, we carried out several tests on a range of challenging publicly available image datasets such as ImageNet, Labeled Faces in the Wild (LFW), and PASCAL-VOC12. Our results show that the proposed method can dissimulate an image into another one with the same size, reaching an embedding capacity of 24 bit per pixel without generating visual or structural artefacts on the host image. In addition, the proposed model is generic, that is, it does not depend on the image's size or the database source.

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“…In recent years there have been several fields of use of CNNs that, nowadays, allow the development of important applications. Some examples are: the antispoofing of the face and iris [11], recognition of highway traffic congestion [12], the image steganography and steganalysis [13], the galaxy morphology classification [14], drone detection, and classification [15,16]. However, the analysis of IIF images, as a whole, and in particular, in regards to the analysis of intensity, is extremely complex and linked to the experience of the immunologist [4].…”

Section: Introductionmentioning

confidence: 99%

Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

et al. 2020

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The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep learning, and in particular the Convolutional Neural Networks (CNNs), have demonstrated their effectiveness in the classification of biomedical images. In this work the efficacy of the CNN fine-tuning method applied to the problem of classification of fluorescence intensity in HEp-2 images was investigated. For this purpose, four of the best known pre-trained networks were analyzed (AlexNet, SqueezeNet, ResNet18, GoogLeNet). The classifying power of CNN was investigated with different training modalities; three levels of freezing weights and scratch. Performance analysis was conducted, in terms of area under the ROC (Receiver Operating Characteristic) curve (AUC) and accuracy, using a public database. The best result achieved an AUC equal to 98.6% and an accuracy of 93.9%, demonstrating an excellent ability to discriminate between the positive/negative fluorescence classes. For an effective performance comparison, the fine-tuning mode was compared to those in which CNNs are used as feature extractors, and the best configuration found was compared with other state-of-the-art works.

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A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis

Cited by 23 publications

References 62 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

CNN Auto-Encoder Network Using Dilated Inception for Image Steganography

Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

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