Chengyun Yang

Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions

In this paper, a steganalysis scheme based on multiple features formed by statistical moments of wavelet characteristic functions is proposed. Our theoretical analysis has pointed out that the defined n-th statistical moment of a wavelet characteristic function is related to the n-th derivative of the corresponding wavelet histogram, and hence is sensitive to data embedding. The selection of the first three moments of the characteristic functions of wavelet subbands of the three-level Haar wavelet decomposition as well as the test image has resulted in total 39 features for steganalysis. The effectiveness of the proposed system has been demonstrated by extensive experimental investigation. The detection rate for Cox et al.s non-blind spread spectrum (SS) data hiding method, Piva et al.s blind SS method, Huang and Shis 8×8 block SS method, a generic LSB method (as embedding capacity being 0.3 bpp), and a generic QIM method (as embedding capacity being 0.1 bpp) are all above 90% over all of the 1096 images in the CorelDraw image database using the Bayes classifier. Furthermore, when these five typical data hiding methods are jointly considered for steganalysis, i.e., when the proposed steganalysis scheme is first trained sequentially for each of these five methods, and is then tested blindly for stego-images generated by all of these methods, the success classification rate is 86%, thus pointing out a new promising approach to general blind steganalysis. The detection results of steganalysis on Jsteg, Outguess and F5 have further demonstrated the effectiveness of the proposed steganalysis scheme.

Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network

In this paper, a general blind image steganalysis system is proposed, in which the statistical moments of characteristic functions of the prediction-error image, the test image, and their wavelet subbands are selected as features. Artificial neural network is utilized as the classifier. The performance of the proposed steganalysis system is significantly superior to the prior arts.

Reversible data hiding using integer wavelet transform and companding technique

This paper presents a novel reversible data-embedding method for digital images using integer wavelet transform and companding technique. This scheme takes advantage of the Laplacian-like distribution of integer wavelet coefficients in high frequency subbands, which facilitates the selection of compression and expansion functions and keeps the distortion small between the marked image and the original one. Experimental results show that this scheme outperforms the state-of-the-art reversible data hiding schemes.

Lossless Data Hiding Using Integer Wavelet Transform and Threshold Embedding Technique

This paper presents a new lossless data hiding method for digital images using integer wavelet transform and threshold embedding technique. Data are embedded into the least significant bit-plane (LSB) of high frequency CDF (2, 2) integer wavelet coefficients whose magnitudes are smaller than a certain predefined threshold. Histogram modification is applied as a preprocessing to prevent overflow/underflow. Experimental results show that this scheme outperforms the prior arts in terms of a larger payload (at the same PSNR) or a higher PSNR (at the same payload)

Reversible data hiding based on wavelet spread spectrum

This paper presents a reversible data hiding method based on wavelet spread spectrum and histogram modification. Using the spread spectrum scheme, we embed data in the coefficients of integer wavelet transform in high frequency subbands. The pseudo bits are also embedded so that the decoder does not need to know which coefficients have been selected for data embedding, thus enhancing data hiding efficiency. Histogram modification is used to prevent the underflow and overflow. Experimental results on some frequently used images show that our method has achieved superior performance in terms of high data embedding capacity and high visual quality of marked images, compared with the existing reversible data hiding schemes.

Effective steganalysis based on statistical moments of wavelet characteristic function

In this paper, an effective steganalysis based on statistical moments of wavelet characteristic function is proposed. It decomposes the test image using two-level Haar wavelet transform into nine subbands (here the image itself is considered as the LL/sub 0/ subband). For each subband, the characteristic function is calculated. The first and second statistical moments of the characteristic functions from all the subbands are selected to form an 18-dimensional feature vector for steganalysis. The Bayes classifier is utilized in classification. All of the 1096 images from the CorelDraw image database are used in our extensive experimental work. With randomly selected 100 images for training and the remaining 996 images for testing, the proposed steganalysis system can steadily achieve a correct classification rate of 79% for the non-blind Spread Spectrum watermarking algorithm proposed by Cox et ai, 88% for the blind Spread Spectrum watermarking algorithm proposed by Piva et ai, and 91% for a generic LSB embedding method, thus indicating significant advancement in steganalysis.

Lossless data hiding using histogram shifting method based on integer wavelets

This paper proposes a histogram shifting method for image lossless data hiding in integer wavelet transform domain. This algorithm hides data into wavelet coefficients of high frequency subbands. It shifts a part of the histogram of high frequency wavelet subbands and thus embeds data by using the created histogram zero-point. This shifting process may be sequentially carried out if necessary. Histogram modification technique is applied to prevent overflow and underflow. The performance of this proposed technique in terms of the data embedding payload versus the visual quality of marked images is compared with that of the existing lossless data hiding methods implemented in the spatial domain, integer cosine transform domain, and integer wavelet transform domain. The experimental results have demonstrated the superiority of the proposed method over the existing methods. That is, the proposed method has a larger embedding payload in the same visual quality (measured by PSNR (peak signal noise ratio)) or has a higher PSNR in the same payload.

High capacity lossless data hiding based on integer wavelet transform

This paper proposes a novel approach to high capacity lossless data hiding based on integer wavelet transform, which embeds high capacity data into the most insensitive bit-planes of wavelet coefficients. Specifically, three high capacity lossless data hiding methods, namely A, B and C are proposed. Method A is the traditional lossless data hiding technique, which can losslessly recover the original image. The capacity can reach 1/10 of the data volume that the original image occupies and histogram modification is used to prevent over/underflow. Method B is not a traditional lossless data hiding technique. It can only losslessly recover the pre-processed image instead of the original image. However, the capacity can reach 1/2 of the data volume that the original image occupies. It has better visual quality than replacing the four least significant bit-planes in the spatial domain. Method C has not only the larger capacity but also better visual quality than Method B. However, it can only losslessly recover the hidden data. These three methods passed through the test on all 1096 images of CorelDraw database. These techniques can be applied to e-government, e-business, e-medical data systems, e-law enforcement and military systems.

A secure internet-based personal identity verification system using lossless watermarking and fingerprint recognition

This paper proposes an internet-based personal identity verification system using lossless data hiding and fingerprint recognition technologies. At the client side, the SHA-256 hash of the original fingerprint image and sensitive personal information are encrypted and embedded into the fingerprint image using an advanced lossless data hiding scheme. At the service provider side, after the hidden data are extracted out, the fingerprint image can be recovered without any distortion due to the usage of the lossless data hiding scheme. Hence, the originality of the fingerprint image can be ensured via hash check. The extracted personal information can be used to obtain the correct fingerprint feature from the database. The fingerprint matching can finally verify the clients identity. The experimental results demonstrate that our proposed system is effective. It can find wide applications in e-banking and e-government systems to name a few.

Traffic measuring and authentication on digital video

Several schemes for traffic monitor on video are developed. The first is the dynamic gap technique by means of digital video processing instead of subsurface electromagnetic detectors. The dynamic background refreshing and threshold selection are obtained by statistical methods. The second is density and queue length measurement in a traffic detection region. The third is interpretation and identification of traffic activities by hidden Markov models and entropy minimization to discover the relationships between hidden causes and observed scenes. The learning system learns a concise model of scene behavior directly from optical flow. The fourth is to authenticate traffic images with robust digital watermarking technique and hiding data in image losslessly.