Natural Steganography in JPEG Compressed Images

Denemark, Tomáš; Bas, Patrick; Fridrich, Jessica

doi:10.2352/issn.2470-1173.2018.07.mwsf-316

Cited by 21 publications

(31 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It can be applied to medical JPEG images, and has better antisteganographic detection performance. Denemark et al [10] embedded a secret message in the quantized DCT coefficients of an image in JPEG format by adding independent realizations of the heteroscedastic noise. Their experiments showed that a large number of payloads could be embedded in monochrome sensors or low-quality JPEG images.…”

Section: Related Workmentioning

confidence: 99%

“…Some works [4,10,20,[22][23][24] may have performed well in the cyberdomain, but their secret messages are difficult to extract in the physical domain in the case of physical distortion. For example, when an image is displayed or printed, the image may not actually be able to be displayed due to the device's own factors (for example, display resolution and rendering, and printer printing effect).…”

Section: Motivationmentioning

confidence: 99%

“…In this experiment, we demonstrate that EasyStego can cross from the cyberdomain to the physical domain. Additionally, we also tested the cross-domain capabilities of other steganography schemes [4,10,[13][14][15]20,[22][23][24] to illustrate their differences with EasyStego. Figure 2 illustrates a set of samples of the secret embedding procedure.…”

Section: Cross-domain Validity Testingmentioning

confidence: 99%

“…Due to the wide distribution and large amount of information, these situations require that the scheme be uncomplicated in its operation and capable of working under various physical conditions (e.g., light conditions, distances, angles) and that the hidden information be robust to physical distortions introduced during the printing process. Some existing digital steganography schemes [4,[9][10][11][12] do not work properly in this scenario because the hidden data are destroyed. Other steganography schemes [13][14][15] based on QR barcodes are not easy to use in the real world because they are not robust to complex physical factors.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

EasyStego: Robust Steganography Based on Quick-Response Barcodes for Crossing Domains

et al. 2019

View full text Add to dashboard Cite

Despite greater attention being paid to sensitive-information leakage in the cyberdomain, the sensitive-information problem of the physical domain remains neglected. Anonymous users can easily access the sensitive information of other users, such as transaction information, health status, and addresses, without any advanced technologies. Ideally, secret messages should be protected not only in the cyberdomain but also in the complex physical domain. However, popular steganography schemes only work in the traditional cyberdomain and are useless when physical distortions of messages are unavoidable. This paper first defines the concept of cross-domain steganography, and then proposes EasyStego, a novel cross-domain steganography scheme. EasyStego is based on the use of QR barcodes as carriers; therefore, it is robust to physical distortions in the complex physical domain. Moreover, EasyStego has a large capacity for embeddable secrets and strong scalability in various scenarios. EasyStego uses an AES encryption algorithm to control the permissions of secret messages, which is more effective in reducing the possibility of sensitive-information leakage. Experiments show that EasyStego has perfect robustness and good efficiency. Compared with the best current steganography scheme based on barcodes, EasyStego has greater steganographic capacity and less impact on barcode data. In robustness tests, EasyStego successfully extracts secret messages at different angles and distances. In the case of adding natural textures and importing quantitative error bits, other related steganography techniques fail, whereas EasyStego can extract secret messages with a success rate of nearly 100%.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Cross-domain Validity Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

EasyStego: Robust Steganography Based on Quick-Response Barcodes for Crossing Domains

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Incorporated some domain knowledge into the network design, such as using high-pass filters for pre-processing, outstanding performance can be obtained.The high-dimensional hand-crafted or deep-learned features with the powerful supervised ML schemes present a great challenge to steganography. A promising strategy for the steganographer is to use side information which is not available to the steganalyst, such as using the camera sensor noise during message embedding [28] and the compression noise during JPEG compression [12]. However, the side information is not always available for all kinds of cover images, especially for those…”

mentioning

confidence: 99%

CNN-Based Adversarial Embedding for Image Steganography

Tang

Tan

et al. 2019

IEEE Trans.Inform.Forensic Secur.

211

132

View full text Add to dashboard Cite

Historically, steganographic schemes were designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML) based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers. However, simply applying perturbations on stego images as adversarial examples may lead to the failure of data extraction and introduce unexpected artefacts detectable by other classifiers. In this paper, we present a steganographic scheme with a novel operation called adversarial embedding, which achieves the goal of hiding a stego message while at the same time fooling a convolutional neural network (CNN) based steganalyzer. The proposed method works under the conventional framework of distortion minimization. Adversarial embedding is achieved by adjusting the costs of image element modifications according to the gradients backpropagated from the CNN classifier targeted by the attack. Therefore, modification direction has a higher probability to be the same as the sign of the gradient. In this way, the so called adversarial stego images are generated. Experiments demonstrate that the proposed steganographic scheme is secure against the targeted adversary-unaware steganalyzer. In addition, it deteriorates the performance of other adversary-aware steganalyzers opening the way to a new class of modern steganographic schemes capable to overcome powerful CNN-based steganalysis. Index TermsSteganography, steganalysis, adversarial machine learning. I. INTRODUCTIONImage steganography is the art and science of concealing covert information within images. It is usually achieved by modifying image elements, such as pixels or DCT coefficients. On the other side of the game, steganalysis aims to reveal the presence of secret information by detecting whether there are abnormal artefacts left by data embedding.The developing history of steganography and steganalysis is rich of interesting stories, as they compete with each other and they benefit and evolve from the competition [1]. The earliest steganographic method was implemented by substituting the least significant bits of image elements with message bits. The stego artefacts introduced by this method can be effectively detected by Chi-squared attack [2], or steganalytic features based on first-order statistics [3]. In this initial phase of the competition, statistical hypothesis testing or a simple linear classifier such as FLD (Fisher Linear Discriminant) could serve the need of steganalysis.The first-order statistics can be restored after data embedding, as done in [4]. The abnormal artefacts in the first-order statistics can also be avoided as in [5], [6]. As a consequence, more powerful steganalytic features based on the second-order statistics [7], [8] were proposed. In this period, advanced machine learning (ML) tools, such as SVM (Support Vector Machine), were operated on high-dimensional features (where the dimension is typically several hundreds). These met...

show abstract

Critical review of bio‐inspired data optimization techniques: An image steganalysis perspective

Johnvictor

Amalanathan

Venkata

et al. 2022

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Image steganalysis involves the discovery of secret information embedded in an image. The common method is blind image steganalysis, which is a twoclass classification problem. Blind steganalysis extracts all possible feature variations in an image due to embedding, select the most appropriate feature data, and then classifies the image. The dimensionality of the extracted image features are high and demand data reduction to identify the most relevant features and to aid accurate classification of an image. The classification is under two classes namely, clean (cover) image and stego (image with embedded

show abstract

Natural Steganography in JPEG Compressed Images

Cited by 21 publications

References 30 publications

EasyStego: Robust Steganography Based on Quick-Response Barcodes for Crossing Domains

EasyStego: Robust Steganography Based on Quick-Response Barcodes for Crossing Domains

CNN-Based Adversarial Embedding for Image Steganography

Critical review of bio‐inspired data optimization techniques: An image steganalysis perspective

Contact Info

Product

Resources

About