Domain Adaptation in Steganalysis for the Spatial Domain

Lin, Li; Newman, Jennifer; Reinders, Stephanie; Guan, Yong Liang; Wu, Min

doi:10.2352/issn.2470-1173.2018.07.mwsf-319

Cited by 7 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless their work concludes with the statement that even with the help of machine learning, a reliable detection of stego images is far away. A publication by Lin et al [13] shows combined error rates (false negative and false positive) of 25% to 40%. A work by Tsang [19] states error rates of as low as 13%, but also only provides a combined error rate.…”

Section: Steganalysis Methodsmentioning

confidence: 99%

Cover-aware Steganalysis

Bunzel

Steinebach

Liu

2021

JCSANDM

View full text Add to dashboard Cite

The increasing digitization offers new ways, possibilities and needs for a secure transmission of information. Steganography and its analysis constitute an essential part of IT-Security. In this work we show how methods of blind-steganalysis can be improved to work in cover-aware scenarios, we will call this non-blind steganalysis. The main objective was to examine how to take advantage of the knowledge of reference images to maximize the accuracy-rate of the analysis. Therefore we evaluated common stego-tools and their embedding algorithms and established a dataset of 353110 images. The images have been applied to test the potency of the improved methods of the non-blind steganalysis. The results show that the accuray can be significantly improved by using cover-images to produce reference images. Also the aggregation of the outcomes has shown to have a positive impact on the accuracy. Particularly noteworthy is the correlation between the qualities of the stego- and coverimages. Only by consindering both, the accuracy could strongly be improved. Interestingly the difference between both qualities also has a deep impact on the results.

show abstract

Section: Steganalysis Methodsmentioning

confidence: 99%

Cover-aware Steganalysis

Bunzel

Steinebach

Liu

2021

JCSANDM

View full text Add to dashboard Cite

show abstract

“…Nonetheless their work concludes with the statement that even with the help of machine learning, a reliable detection of stego images is far away. A recent publication by Lin et al [8] shows combined error rates (false negative and false positive) of 25% to 40%. A work by Tsang [13] states error rates of as low as 13%, but also only provides a combined error rate.…”

Section: Further Steganalysismentioning

confidence: 99%

The Need for Steganalysis in ImageDistribution Channels

Steinebach

Liu

Ester

2019

JCSM

View full text Add to dashboard Cite

The rise of social networks during the last 10 years has created a situation in which up to 100 million new images and photographs are uploaded and shared by users every day. This environment poses an ideal background for those who wish to communicate covertly by the use of steganography. It also creates a new set of challenges for steganalysts, who have to shift their field of work away from a purely scientific laboratory environment and into a diverse real-world scenario, while at the same time having to deal with entirely new problems, such as the detection of steganographic channels or the impact that even a low false positive rate has when investigating the millions of images which are shared every day on social networks. We evaluate how to address these challenges with traditional steganographic and statistical methods, rather than using high performance computing and machine learning. To achieve this we first analyze the steganographic algorithm F5 applied to images with a high degree of diversity, as would be seen in a typical social network. We show that the biggest challenge lies in the detection of images whose payload is less then 50% of the available capacity of an image. We suggest new detection methods and apply these to the problem of channel detection in social network. We are able to show that using our attacks we are able to detect the majority of covert F5 channels after a mix containing 10 stego images has been classified by our scheme.

show abstract

“…The latter we choose because it consists of images from mobile devices. As increasingly more images "in-the-wild" originate from cell phone cameras, it is important to collect data from these sources [Lin et al (2018b); Chen et al The BOSSbase dataset contains 10,000 RAW images from seven digital still cameras. We convert the RAW images to TIFF images in Photoshop.…”

Section: Image Datasetsmentioning

confidence: 99%

Statistical methods for digital image forensics: Algorithm mismatch for blind spatial steganalysis and score-based likelihood ratios for camera device identification

Reinders¹

Self Cite

View full text Add to dashboard Cite

The number and availability of steganographic embedding algorithms continues to grow. Many traditional blind steganalysis frameworks require training examples from every embedding algorithm, but collecting, storing and processing representative examples of each algorithm can quickly become untenable. Our motivation for this paper is to create a straight-forward, non-data-intensive framework for blind steganalysis that only requires examples of cover images and a single embedding algorithm for training. Our blind steganalysis framework addresses the case of algorithm mismatch, where a classifier is trained on one algorithm and tested on another, with four spatial embedding algorithms: LSB matching, MiPOD, S-UNIWARD and WOW. We use RAW image data from the BOSSbase Database and and six iPhone devices collected for the StegoAppDB project. Ensemble Classifiers with Spatial Rich Model features are trained on a single embedding algorithm and tested on each of the four algorithms. Classifiers trained on MiPOD, S-UNIWARD and WOW data achieve decent error rates when testing on all four algorithms. Most notably, an Ensemble Classifier with an adjusted decision threshold trained on LSB matching data achieves decent detection results on MiPOD, S-UNIWARD and WOW data.

show abstract

Domain Adaptation in Steganalysis for the Spatial Domain

Cited by 7 publications

References 0 publications

Cover-aware Steganalysis

Cover-aware Steganalysis

The Need for Steganalysis in ImageDistribution Channels

Statistical methods for digital image forensics: Algorithm mismatch for blind spatial steganalysis and score-based likelihood ratios for camera device identification

Contact Info

Product

Resources

About