Further Study on YASS: Steganography Based on Randomized Embedding to Resists Blind Steganalysis

1. Further Study on YASS:Steganography Based on Randomized Embedding to Resists Blind Steganalysis *Mayachitra, Inc. 5266 Hollister Ave. Santa Barbara, CA-93111, USA http://www.mayachitra.com A. Sarkar+, K. Solanki*, and B. Manjunath+ + Vision Research Laboratory Department of Electrical and Computer Engineering University of California, Santa Barbara, CA 93106, USA http://vision.ece.ucsb.edu

2. Further Study on YASS SteganographicScheme: Methods that enable secret communication. The very existence of communication is not known to a third party. Needinnocent looking cover objects, in which the secret message is embedded. • Perceptual transparency • Statistical transparency YASS: Yet Another SteganographicScheme

3. Steganalysis: Winning the battle? • Blind Statistical steganalysis • Uses supervised learning on specific image features • Self-calibration mechanism used to ensure that features capture changes due to embedding only. • E.g. Cropping a few pixel rows and/or columns • Recent results close to perfect • Papers presented in this Conference • [Pevny and Fridrich ‘07]: can reliably determine which stego scheme was used (out of 5) with >95% accuracy

4. A recipe for resisting blind steganalysis YASS: “Stirmark of Steganography” • Idea: Desynchronize the steganalyst by randomizing the embedding locations • Disables the self-calibration process • But, must advertize or ship the image in a standard format (such as JPEG) • Causes errors in the recovered bits • Use erasures and errors correcting codes • Previously employed for high-volume hiding

5. A peek at the YASS Results [IH’07] Can resist several recent steganalysis techniques • JPEG steganalysis with self-calibration • Pevny and Fridrich’s23-dim features (SPIE’06) and their more recent 274-dim feature (SPIE’07) • Farid’s 72-dim features • DCT histogram-based features • Spatial domain steganalysis • Xuan et al’s39-dim features based on wavelet characteristic functions (IH ‘05) • JPEG steganalysis based on above features • Chen et al’s324-dim features (ICIP ‘06)

6. This Year’s News [SPIE’08] • Proposed modifications to the embedding process • Good improvement achieved in the embedding rate! • Can outperform F5 in terms of the rate of embedding at equivalent detection rates

7. Outline • Introduction • Resisting Blind Steganalysis • YASS Recap • Improving the Embedding Rate • Results • Discussion

8. Outline • Introduction • Resisting Blind Steganalysis • Why blind steganalysis works? • Can we do something? • YASS Recap • Improving the Embedding Rate • Results • Discussion

9. Blind Steganalysis: Key Ingredients • Self-calibration mechanism • Used to estimate the cover image statistics from the stego image • For JPEG steganography: Crop a few pixel rows or columns and recompress • Features capturing cover memory • Most stego scheme hide data on a per-symbol basis • Higher order dependencies harder to match or restore • Powerful machine learning • Ensures that even the slightest statistical variation in the features is learned by the machine

10. Blind steganalysis is quite successful • Self-calibration process is perhaps the most important ingredient • Derived features are insensitive to image content, but quite sensitive to embedding changes • Steganalysis successful in spite of unavailability of universal image models • Results presented in [Pevny and Fridrich, SPIE ‘07] are close to perfect!

11. So, What can the steganographer do? • Preserve all the features of the image… • Is this practically feasible? • How does it affect the embedding rate? • But, lets not forget: The steganalyst must depend on the stego image to estimate the cover image statistics • Way out: Embed data in a way that distorts the steganalyst’s estimate of the cover image statistics

12. Distorting Steganalyst’s Estimate • Hiding with high embedding strength • Cover image statistics can no longer be reliably derived from the available stego image • Also observed and reported in recent work by Kharrazi, Sencar, and Memon (ICIP ‘06) • Randomized hiding • The algorithm to estimate the cover statistics can be effectively disabled • Can randomize the hiding location, the choice of transform domain, the coefficient, or even the hiding method Disadvantages Likelihood of high perceptual distortion Possibility of data being detected by universal image models Our Choice! Simple implementation explored in this work: Hide in random locations

13. Outline • Introduction • Resisting Blind Steganalysis • YASS Recap • Improving the Embedding Rate • Results • Discussion

14. YASS for JPEG Steganography • Idea: Embed data in randomized block locations • The blocks do not coincide with the JPEG 8x8 grid • Errors caused due to initial JPEG compression after embedding data • Use erasures and errors correcting codes

15. YASS Embedding: JPEG Grid 8 pixels

16. YASS Embedding Example grid used in embedding Randomized block location B x B block Here, B=10 B is called “big block size”

17. Reduction in Embedding Rate • Wasted real estate of the image • Due to choice of bigger blocks • Can reduce this wastage by putting more than one blocks in larger bigger blocks • Eg 16 blocks in 34x34 sized block • Errors due to initial JPEG compression • Use erasures and errors correcting codes • Previously employed in [Solanki et al, Trans. Image Pro. Dec 2004]

18. Outline • Introduction • Resisting Blind Steganalysis • YASS Recap • Improving the Embedding Rate • Further parameter variation • Iterative embedding • Results • Discussion

19. Improving the Embedding Rate • Further randomization • A natural extension of original YASS • Vary other hiding parameters, such as quantization matrix • Attack-aware iterative embedding • JPEG compression occurs at the encoder • Errors occur due to this JPEG attack • Corrected via an iterative embedding strategy

20. Further Randomization • Randomness in What? • Location of hiding blocks (DONE) • Choice of hiding bands per block • Choice of transform domain per block • Choice of design quality factor per block (TRIED) • How to vary design quality factor? • Random (arbitrary) • Image adaptive (systematic/intelligent choice)

21. How to vary design QF • Assign that design QF which allows maximum hiding for that block with maximum hiding capacity (higher number of non-zero DCT terms, i.e. more variance in general) • Design QF of 50 – use for [4,inf) zone • Design QF of 60 – use for [1,4) zone • Design QF of 70 – use for [0,1) zone

22. Improving the Embedding Rate • Further randomization • A natural extension of original YASS • Vary other hiding parameters, such as quantization matrix • Attack-aware iterative embedding • JPEG compression occurs at the encoder • Errors occur due to this JPEG attack • Corrected via an iterative embedding strategy

23. Iterative Embedding (YASS-M1) • Biggest reason for lower embedding rate: • Raw errors caused due to initial JPEG compression • Good News: This JPEG attack occurs at the encoder itself, so is “known” • Bad News: The attack cannot be systematically characterized • Solution: Iterative embedding • “Hide-attack-hide again” • Improves the embedding rate without affecting detectability

24. YASS-M1 Original Approach YASS Embedding JPEG Compression at output QF Uncompressed image Stego image YASS-M1 Approach Correction YASS Embedding JPEG Compression at output QF YASS Embedding Stego image Uncompressed image Only locations where errors are introduced by JPEG compression will be corrected

25. Outline • Introduction • Resisting Blind Steganalysis • YASS Recap • Improving the Embedding Rate • Results • Discussion

26. Results • Experimental Set-up • Choosing the parameters for evaluation • Mixture based scheme results • YASS-M1 results • Comparison with F5 • Dependency on the image size

27. Experimental Set-up Steganalysis: Compute probability of detection • PF-274: Pevny and Fridrich’s 274-dimensional feature vector that merges Markov and DCT features • Chen-324: JPEG steganalysis based on statistical moments of wavelet characteristic functions Database of images Testing Hide Data Training

28. Results • Experimental Set-up • Choosing the parameters for evaluation • Mixture based scheme results • YASS-M1 results • Comparison with F5

29. Choosing the parameters for evaluation • Goal: Compare the hiding rate of the schemes proposed here with other methods • Keep the same detection rate • We consider Pd of 0.60 (or less) as “undetectable” • Choose embedding parameters of different schemes such that detection rate is close to 0.60 • Then compare the achieved hiding rates

30. Results • Experimental Set-up • Choosing the parameters for evaluation • Mixture based scheme results • YASS-M1 results • Comparison with F5 • Dependency on the image size

31. Mixture based schemes: Embedding and detection rates • YASS: Original YASS scheme with QFh=60 • Mixture-random (50-60-70-rand): Random selection of quality factors from 50, 60, and 70 • Mixture-variance (50-60-70-var): Adaptive selection of quality factor to use based on block variance • Note: Qfa = 75 in these experiments

32. Results • Experimental Set-up • Choosing the parameters for evaluation • Mixture based scheme results • YASS-M1 results • Comparison with F5 • Dependency on the image size

33. YASS-M1: Embedding and detection rates Significant improvement achieved in embedding rate!

34. Results • Experimental Set-up • Choosing the parameters for evaluation • Mixture based scheme results • YASS-M1 results • Comparison with F5 • Dependency on the image size

35. Comparison with F5 Our schemes outperform F5 in terms of hiding rate (at equivalent detection rates)

36. Comparison with F5

37. Results • Experimental Set-up • Choosing the parameters for evaluation • Mixture based scheme results • YASS-M1 results • Comparison with F5 • Dependency on the image size

38. Dependency on the image size • Full size images: either 1600x1200 or 2592x1924

39. Outline • Introduction • Resisting Blind Steganalysis • YASS Recap • Improving the Embedding Rate • Results • Discussion

40. YASS and State-of-the-art • Original YASS scheme (Monday morning news) • Equivalentlevel of steganographic security can be achieved by using matrix embedding, nsF5 etc. • YASS-M1 changes that! • Might provide better trade-off • Focus of current state-of-the-art stego methods • Make as fewer changes as possible • YASS schemes make large number of changes, yet cannot be detected

41. YASS and State-of-the-art (Cont.) • Low embedding efficiency of YASS can be good! • An advantage of YASS is that it can provide robustness against distortion constrained attacks • Can enable active steganography • Active steganography desirable in many scenarios • Adversary controlling the email/web server can recompress all the images

42. Thank you All Invited to Submission deadline: Feb 4th Conference:May 19th to 21st