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A Brief Survey on Deep Learning Based Data Hiding, Steganography and Watermarking.

Abstract: Data hiding is the art of concealing messages with limited perceptual changes. Recently, deep learning has provided enriching perspectives for it and made significant progress. In this work, we conduct a brief yet comprehensive review of existing literature and outline three meta-architectures. Based on this, we summarize specific strategies for various applications of deep hiding, including steganography, light field messaging and watermarking. Finally, further insight into deep hiding is provided through incorporating the perspective of adversarial attack.

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Topics: Information hiding (55%), Steganography (55%), Digital watermarking (53%)
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Open accessPosted Content
Honglei Zhang1, Hu Wang2, Yuanzhouhan Cao1, Chunhua Shen2  +1 moreInstitutions (2)
Abstract: Data hiding is the procedure of encoding desired information into the cover image to resist potential noises while ensuring the embedded image has few perceptual perturbations from the original one. Recently, with the tremendous successes gained by deep neural networks in various fields, the researches of data hiding with deep learning models have attracted an increasing number of attentions. In the data hiding task, each pixel of cover images should be treated differently since they have divergent tolerabilities. The neglect of considering the sensitivity of each pixel will inevitably affect the model robustness for information hiding. Targeting this problem, we propose a novel deep data hiding scheme with Inverse Gradient Attention (IGA), combing the ideas of adversarial learning and attention mechanism to endow different sensitivities for different pixels. With the proposed component, the model can spotlight pixels with more robustness for data hiding. Empirically, extensive experiments show that the proposed model outperforms the state-of-the-art methods on two prevalent datasets under multiple evaluations. Besides, we further identify and discuss the connections between the proposed inverse gradient attention and high-frequency regions within images.

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2 Citations


Open accessPosted Content
Abstract: Deep hiding, embedding images into another using deep neural networks, has shown its great power in increasing the message capacity and robustness. In this paper, we conduct an in-depth study of state-of-the-art deep hiding schemes and analyze their hidden vulnerabilities. Then, according to our observations and analysis, we propose a novel ProvablE rEmovaL attack (PEEL) using image inpainting to remove secret images from containers without any prior knowledge about the deep hiding scheme. We also propose a systemic methodology to improve the efficiency and image quality of PEEL by carefully designing a removal strategy and fully utilizing the visual information of containers. Extensive evaluations show our attacks can completely remove secret images and has negligible impact on the quality of containers.

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Open accessPosted Content
Abstract: 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.

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Topics: Digital watermarking (61%), Information hiding (61%), Steganography (59%)

Open accessPosted Content
Ruowei Wang1, Chenguo Lin1, Qijun Zhao1, Feiyu Zhu1Institutions (1)
Abstract: Digital watermarking has been widely used to protect the copyright and integrity of multimedia data. Previous studies mainly focus on designing watermarking techniques that are robust to attacks of destroying the embedded watermarks. However, the emerging deep learning based image generation technology raises new open issues that whether it is possible to generate fake watermarked images for circumvention. In this paper, we make the first attempt to develop digital image watermark fakers by using generative adversarial learning. Suppose that a set of paired images of original and watermarked images generated by the targeted watermarker are available, we use them to train a watermark faker with U-Net as the backbone, whose input is an original image, and after a domain-specific preprocessing, it outputs a fake watermarked image. Our experiments show that the proposed watermark faker can effectively crack digital image watermarkers in both spatial and frequency domains, suggesting the risk of such forgery attacks.

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Topics: Watermark (65%), Digital watermarking (61%), Digital image (54%)

Proceedings ArticleDOI: 10.1109/ECAI52376.2021.9515076
Ruxandra Fratila1, Luciana Morogan1Institutions (1)
01 Jul 2021-
Abstract: Seeking to take advantage of the innovations brought by machine learning, a field in a continuous movement and development, the present paper aims to enhance a practice that has been used since ancient times: steganography. Thereby, we targeted the implementation of a system aimed to hide image-type messages with the aid of deep neural networks. We followed a baseline model designed according to the recommendations stated into the state-of-the-art section. Then, we progressively developed three new models, each adding a new improvement on top of the previous one.

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Topics: Steganography (55%), Deep learning (52%)
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Open accessJournal ArticleDOI: 10.1109/TIFS.2012.2190402
Jessica Fridrich1, Jan Kodovsky1Institutions (1)
Abstract: We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters. In contrast to previous approaches, we make the model assembly a part of the training process driven by samples drawn from the corresponding cover- and stego-sources. Ensemble classifiers are used to assemble the model as well as the final steganalyzer due to their low computational complexity and ability to efficiently work with high-dimensional feature spaces and large training sets. We demonstrate the proposed framework on three steganographic algorithms designed to hide messages in images represented in the spatial domain: HUGO, edge-adaptive algorithm by Luo , and optimally coded ternary ±1 embedding. For each algorithm, we apply a simple submodel-selection technique to increase the detection accuracy per model dimensionality and show how the detection saturates with increasing complexity of the rich model. By observing the differences between how different submodels engage in detection, an interesting interplay between the embedding and detection is revealed. Steganalysis built around rich image models combined with ensemble classifiers is a promising direction towards automatizing steganalysis for a wide spectrum of steganographic schemes.

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Topics: Steganalysis (62%), Steganography (54%), Feature extraction (52%) ... read more

1,145 Citations


Journal ArticleDOI: 10.1109/LSP.2016.2548421
Guanshuo Xu1, Hanzhou Wu2, Yun-Qing Shi1Institutions (2)
Abstract: Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis. In this letter, we report a CNN architecture that takes into account knowledge of steganalysis. In the detailed architecture, we take absolute values of elements in the feature maps generated from the first convolutional layer to facilitate and improve statistical modeling in the subsequent layers; to prevent overfitting, we constrain the range of data values with the saturation regions of hyperbolic tangent ( TanH ) at early stages of the networks and reduce the strength of modeling using $1\times1$ convolutions in deeper layers. Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL. The results have implied that well-designed CNNs have the potential to provide a better detection performance in the future.

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Topics: Steganalysis (60%), Convolutional neural network (59%), Deep learning (57%) ... read more

359 Citations


Proceedings ArticleDOI: 10.1117/12.2083479
Yinlong Qian1, Jing Dong, Wei Wang, Tieniu TanInstitutions (1)
04 Mar 2015-electronic imaging
Abstract: Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

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Topics: Steganalysis (66%), Deep learning (59%), Feature learning (58%) ... read more

345 Citations


Journal ArticleDOI: 10.1109/TIFS.2017.2710946
Jian Ye1, Jiangqun Ni1, Yang Yi1Institutions (1)
Abstract: Nowadays, the prevailing detectors of steganographic communication in digital images mainly consist of three steps, ie, residual computation, feature extraction, and binary classification In this paper, we present an alternative approach to steganalysis of digital images based on convolutional neural network (CNN), which is shown to be able to well replicate and optimize these key steps in a unified framework and learn hierarchical representations directly from raw images The proposed CNN has a quite different structure from the ones used in conventional computer vision tasks Rather than a random strategy, the weights in the first layer of the proposed CNN are initialized with the basic high-pass filter set used in the calculation of residual maps in a spatial rich model (SRM), which acts as a regularizer to suppress the image content effectively To better capture the structure of embedding signals, which usually have extremely low SNR (stego signal to image content), a new activation function called a truncated linear unit is adopted in our CNN model Finally, we further boost the performance of the proposed CNN-based steganalyzer by incorporating the knowledge of selection channel Three state-of-the-art steganographic algorithms in spatial domain, eg, WOW, S-UNIWARD, and HILL, are used to evaluate the effectiveness of our model Compared to SRM and its selection-channel-aware variant maxSRMd2, our model achieves superior performance across all tested algorithms for a wide variety of payloads

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Topics: Steganalysis (55%), Feature extraction (55%), Deep learning (53%) ... read more

308 Citations


Open accessJournal ArticleDOI: 10.1109/TIFS.2018.2871749
Mehdi Boroumand1, Mo Chen1, Jessica Fridrich1Institutions (1)
Abstract: Steganography detectors built as deep convolutional neural networks have firmly established themselves as superior to the previous detection paradigm – classifiers based on rich media models. Existing network architectures, however, still contain elements designed by hand, such as fixed or constrained convolutional kernels, heuristic initialization of kernels, the thresholded linear unit that mimics truncation in rich models, quantization of feature maps, and awareness of JPEG phase. In this work, we describe a deep residual architecture designed to minimize the use of heuristics and externally enforced elements that is universal in the sense that it provides state-of-the-art detection accuracy for both spatial-domain and JPEG steganography. The key part of the proposed architecture is a significantly expanded front part of the detector that “computes noise residuals” in which pooling has been disabled to prevent suppression of the stego signal. Extensive experiments show the superior performance of this network with a significant improvement, especially in the JPEG domain. Further performance boost is observed by supplying the selection channel as a second channel.

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Topics: Steganalysis (60%), Steganography (56%), JPEG (56%) ... read more

240 Citations


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