Yet Another Steganographic Scheme Research Articles

Resisting Blind Steganalysis in Real Time Covert Communication

In this work, we review recent developments in the field of steganography keeping real time covert communication applications in mind. We then propose a microarchitecture to transform the existing one of most successful Steganographic technique, Yet Another Steganographic Scheme (YASS) to the hardware platform for satisfying the current Steganographic application needs. This hardware will be able to resist the Self-calibration based blind Steganalysis attacks, which are the most successful attacks in breaking any Steganographic technique till now. General Terms Information security, steganography, data hiding, watermarking.

Improved Approaches with Calibrated Neighboring Joint Density to Steganalysis and Seam-Carved Forgery Detection in JPEG Images

Steganalysis and forgery detection in image forensics are generally investigated separately. We have designed a method targeting the detection of both steganography and seam-carved forgery in JPEG images. We analyze the neighboring joint density of the DCT coefficients and reveal the difference between the untouched image and the modified version. In realistic detection, the untouched image and the modified version may not be obtained at the same time, and different JPEG images may have different neighboring joint density features. By exploring the self-calibration under different shift recompressions, we propose calibrated neighboring joint density-based approaches with a simple feature set to distinguish steganograms and tampered images from untouched ones. Our study shows that this approach has multiple promising applications in image forensics. Compared to the state-of-the-art steganalysis detectors, our approach delivers better or comparable detection performances with a much smaller feature set while detecting several JPEG-based steganographic systems including DCT-embedding-based adaptive steganography and Yet Another Steganographic Scheme (YASS). Our approach is also effective in detecting seam-carved forgery in JPEG images. By integrating calibrated neighboring density with spatial domain rich models that were originally designed for steganalysis, the hybrid approach obtains the best detection accuracy to discriminate seam-carved forgery from an untouched image. Our study also offers a promising manner to explore steganalysis and forgery detection together.

On YASS’s Non-monotonic Security Performance

Recently, researchers have discovered unexpected bumps in the detection rate curve of yet another steganographic scheme (YASS). We refer to this abnormal phenomenon as non-monotonic security performance. This paper first analyzes this abnormality and points out that it is caused by the non-uniformity in probability of coincidence of $$8\,\times \,8$$ 8 × 8 embedding blocks and $$8\,\times \,8$$ 8 × 8 JPEG blocks. Based on this observation, we propose that further randomizing data embedding positions can overcome the non-monotonic security performance. Experimental results have demonstrated a complete removal of bumps in the detection rate curve of YASS after further randomization.

YASS Steganalysis Based on Local Randomness

In the paper,a new steganalysis scheme for attacking Yet Another Steganographic Scheme(YASS)is proposed.Based on the size of B-blocks,the locations of the 8×8 embedding sub-blocks are determined.The features are calculated from four statistics of local randomness sequences of the low frequency coefficients in the 8×8 embedding sub-blocks and Fisher linear discriminant is exploited to classify.The experiments on YASS steganography and its further extensions show the method can detect stego images reliably,and the detection accuracy exceeds those of its closest competitors obviously.

An Experimental Study on the Security Performance of YASS

This paper presents an experimental study on the security performance of Yet Another Steganographic Scheme (YASS). It reports: 1) YASS's security performance with different input images, i.e., uncompressed images and JPEG compressed images; 2) YASS's security performance compared with two other JPEG steganographic schemes MB1 and F5; and 3) some experimental results about extended YASS.

Matrix Embedding With Pseudorandom Coefficient Selection and Error Correction for Robust and Secure Steganography

In matrix embedding (ME)-based steganography, the host coefficients are minimally perturbed such that the transmitted bits fall in a coset of a linear code, with the syndrome conveying the hidden bits. The corresponding embedding distortion and vulnerability to steganalysis are significantly less than that of conventional quantization index modulation (QIM)-based hiding. However, ME is less robust to attacks, with a single host bit error leading to multiple decoding errors for the hidden bits. In this paper, we employ the ME-RA scheme, a combination of ME-based hiding with powerful repeat accumulate (RA) codes for error correction, to address this problem. A key contribution of this paper is to compute log likelihood ratios for RA decoding, taking into account the many-to-one mapping between the host coefficients and an encoded bit, for ME. To reduce detectability, we hide in randomized blocks, as in the recently proposed Yet Another Steganographic Scheme (YASS), replacing the QIM-based embedding in YASS by the proposed ME-RA scheme. We also show that the embedding performance can be improved by employing punctured RA codes. Through experiments based on a couple of thousand images, we show that for the same embedded data rate and a moderate attack level, the proposed ME-based method results in a lower detection rate than that obtained for QIM-based YASS.