Sensor pattern noise (SPN) extracted from digital images has been proved to be a unique fingerprint of digital camera.
However, sensor pattern noise can be contaminated largely in frequency domain by image detail from scene according to
Li's work and non-unique artifacts of on-sensor signal transfer, sensor design, color interpolation according to Chen et
al's work, the source camera identification performance based on SPN needs to be improved especially for small image
block. Motivated by their works, in order to lessen the effect of these contaminations, the unique SPN fingerprint for
identifying one specific camera is assumed to be a white noise which has a flat frequency spectrum, so the SPN extracted
from an image is whitened first to have a flat frequency spectrum, then inputted to the mixed correlation detector.
Source camera identification is the detection of the existence of the camera reference SPN in the SPN extracted from a
single image. Compared with the correlation detection approach and Li's model based approaches on 7 cameras, 1400
photos totally, each camera is responsible for 200, the experimental results show that the proposed mixed correlation
detection enhances the receiver operating characteristic (ROC) performance of source camera identification, especially
greatly raises the detection rate (true positive rate) in the case of trustworthy identification which is with a low false
positive rate. For example, the proposed mixed correlation detection raises the true positive rate from 78% to 93% at
zero false positive rate on image blocks of 256x256 pixels cropped from the center of the 1400 photos. The proposed
mixed correlation detection also has large advantage to resist JPEG compression with low quality factor. Fridrich's
group has proposed two reference SPN extraction methods which are the noise residues averaging and the maximum
likelihood estimation method. They are compared from the aspect of ROC performance associated with the correlation
detection and mixed correlation detection respectively. It is observed that the combination of mixed correlation detection
and reference SPN extraction method of noise residues averaging achieves the best performance. We also demonstrate an
image management application of the proposed SPN detection method for the news agent. It shows that the detection
method discriminates the positive samples from a large number of negative samples very well on image bock size of
MP3 is the most popular audio format nowadays in our daily life, for example music downloaded from the Internet and
file saved in the digital recorder are often in MP3 format. However, low bitrate MP3s are often transcoded to high bitrate
since high bitrate ones are of high commercial value. Also audio recording in digital recorder can be doctored easily by
pervasive audio editing software. This paper presents two methods for the detection of double MP3 compression. The
methods are essential for finding out fake-quality MP3 and audio forensics. The proposed methods use support vector
machine classifiers with feature vectors formed by the distributions of the first digits of the quantized MDCT (modified
discrete cosine transform) coefficients. Extensive experiments demonstrate the effectiveness of the proposed methods.
To the best of our knowledge, this piece of work is the first one to detect double compression of audio signal.
This paper takes the task of image steganalysis as a texture classification problem. The impact of steganography to an
image is viewed as the alteration of image texture in a fine scale. Specifically, stochastic textures are more likely to
appear in a stego image than in a cover image from our observation and analysis. By developing a feature extraction
technique previously used in texture classification, we propose a set of universal steganalytic features, which are
extracted from the normalized histograms of the local linear transform coefficients of an image. Extensive experiments
are conducted to make comparison of our proposed feature set with some existing universal steganalytic feature sets on
gray-scale images by using Fisher Linear Discriminant (FLD). Some classical non-adaptive spatial domain
steganographic algorithms, as well as some newly presented adaptive spatial domain steganographic algorithms that have
never been reported to be broken by any universal steganalytic algorithm, are used for benchmarking. We also report the
detection performance on JPEG steganography and JPEG2000 steganography. The comparative experimental results
show that our proposed feature set is very effective on a hybrid image database.
With sophisticated video editing technologies, it is becoming increasingly easy to tamper digital video without
leaving visual clues. One of the common tampering operations on video is to remove some frames and then
re-encode the resulting video. In this paper, we propose a new method for detecting this type of tampering by
exploring the temporal patterns of the block artifacts in video sequences. We show that MPEG compression
introduces different block artifacts into various types of frames and that the strength of the block artifacts as
a function over time has a regular pattern for a given group of pictures (GOP) structure. When some frames
are removed from an MPEG video file and the file is then recompressed, the block artifacts introduced by the
previous compression would remain and affect the average of block artifact strength of the recompressed one in
such a way that depends on the number of deleted frames and the type of GOP used previously. We propose a
feature curve to reveal the compression history of an MPEG video file with a given GOP structure, and use it as
evidence to detect tampering. Experimental results evaluated on common video benchmark clips demonstrate
the effectiveness of the proposed method.
Geometrical transforms such as time-scale modification (TSM), random removal(RR), random duplication(RD), and
cropping, are of common operations on audio signals while presents many challenges to robust audio watermarking. The
existing algorithms aiming at solving the geometrical distortions have various drawbacks e.g. high false alarm
probability, heavy computation load, small data hiding capacity, and low robustness performance. In this paper an audio
watermarking algorithm based on dyadic wavelet transform robust to geometrical distortions is proposed. Watermark
synchronization is achieved using the geometrical invariant properties of dyadic wavelet transform. A well-designed
coding scheme is proposed for lowering the bit error rate of the watermark. The experimental results show that the
watermark is robust to geometrical transforms and other common operations. Compared with other existing algorithms
the proposed algorithm has several advantages of high robustness, large data hiding capacity and low computation load.
In some applications such as real-time video applications, watermark detection needs to be performed in real time.
To address image watermark robustness against geometric transformations such as the combination of rotation, scaling,
translation and/or cropping (RST), many prior works choose exhaustive search method or template matching method to
find the RST distortion parameters, then reverse the distortion to resynchronize the watermark. These methods typically
impose huge computation burden because the search space is typically a multiple dimensional space. Some other prior
works choose to embed watermarks in an RST invariant domain to meet the real time requirement. But it might be
difficult to construct such an RST invariant domain. Zernike moments are useful tools in pattern recognition and image
watermarking due to their orthogonality and rotation invariance property. In this paper, we propose a fast watermark
resynchronization method based on Zernike moments, which requires only search over scaling factor to combat RST
geometric distortion, thus significantly reducing the computation load. We apply the proposed method to circularly
symmetric watermarking. According to Plancherel's Theorem and the rotation invariance property of Zernike moments,
the rotation estimation only requires performing DFT on Zernike moments correlation value once. Thus for RST attack,
we can estimate both rotation angle and scaling factor by searching for the scaling factor to find the overall maximum
DFT magnitude mentioned above. With the estimated rotation angle and scaling factor parameters, the watermark can be
resynchronized. In watermark detection, the normalized correlation between the watermark and the DFT magnitude of
the test image is used. Our experimental results demonstrate the advantage of our proposed method. The watermarking
scheme is robust to global RST distortion as well as JPEG compression. In particular, the watermark is robust to
print-rescanning and randomization-bending local distortion in Stirmark 3.1.
The ambiguity attack is to derive a valid watermark from a medium to defeat the ownership claim of the real owner. Most of the research suggests that it is difficult to design a provably secure non-ambiguity watermarking without a trusted third party. Recently, Li and Chang have provided a specific blind additive spread spectrum watermarking scheme as an example that is provably non-ambiguous. However, the proposed watermarking needs the length of watermark n > 3.07 × 109 according to our analysis. In this paper, a framework for quantization based watermarking schemes and non-blind spread spectrum watermarking scheme to achieve non-ambiguity is proposed. As a result, many of the existent watermarking schemes can achieve provable non-invertibility via using this framework, and an nonambiguity ownership verification protocol without a trusted third party may be constructed. We have obtained the close form solution of false positive rate for the underlying quantization based schemes and spread spectrum watermarking schemes (both blind and non-blind). The length of key of pseudo-random sequence generator (PRSG) is extended to m = c × n, the cardinality of the valid watermark set is extended to &verline;W&verline; = 2m = 2c,n, thus leading to more security to exhaustive searching attack than the Li and Chang's scheme, which has m = &sqrt;n. In addition, the required length of watermark becomes much shorter than that required in the Li and Chang's scheme. At last, we propose a noninvertible and robust quantization-based watermarking scheme with the length of watermark being n=1024.
This paper presents a robust watermarking scheme based on multi-band wavelet and principle component analysis (PCA) technique. Incorporating the PCA technique, the developed blind watermarking in multi-band wavelet domain can successfully resist common signal processing such as JPEG compression with quality factor as low as 15, and geometric distortion such as cropping (cropped by 50%). Different from many other watermarking schemes, in which the watermark detection threshold is chosen empirically, the false positive of the proposed watermarking scheme could be calculated, so watermark detection threshold could be chosen based only on the target false positive. Comparing with similar watermarks in conventional two-band wavelet domain, greater perceptual transparency and more robustness could be achieved for the proposed watermarking scheme. The parameterized multi-band wavelet also leads to a more secure embedding domain, which makes attacks more difficult.
Network Keys Exchange Facility (NKEF) is a kind of negotiatory protocol. With it, network user can correspond with each other in ID authentication mode, encryption styles and secure connect time. It's one of research hotspots of network security problem. In this paper, Shamir protocol based scheme for Secret Transmission of digital image is proposed. According to the exchangeable character of encrypting operator, we give the scheme, which can overcome the traditional method's disadvantages including insecurity and that the amount of key is too large. This method takes full advantage ofShamir protocol' s skillful idea.