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Source camera identification (SCI) is an important topic in image forensics. One of the most effective fingerprints for linking an image to its source camera is the sensor pattern noise, which is estimated as the difference between the content and its denoised version. It is widely believed that the performance of the sensor-based SCI heavily relies on the denoising filter used. This study proposes a novel sensor-based SCI method using content adaptive guided image filter (CAGIF). Thanks to the low complexity nature of the CAGIF, the proposed method is much faster than the state-of-the-art methods, which is a big advantage considering the potential real-time application of SCI. Despite the advantage of speed, experimental results also show that the proposed method can achieve comparable or better performance than the state-of-the-art methods in terms of accuracy.

As one of the most interesting nonlinear systems with nonholonomic constraint, Brockett integrator is researched widely at present in nonlinear control field. Zhang-gradient (ZG) method is a type of powerful method for real-time control problems solving. In this paper, the nonlinear system of two-input Brockett integrator is investigated. By exploiting the ZG method, three types of ZG controller groups (specifically, two types of zlgO-zlgO controller groups and one type of zlgO-zlgl controller group) are designed for two-output tracking of Brockett integrator. Computer simulations are conducted to substantiate the feasibility and effectiveness of the two-input Brockett integrator equipped with the designed ZG controller groups for outputs tracking. The simulation results verify that the ZG controller in form of u(t) can handle the division-by-zero problem successfully. Besides, the relation between two output errors of ZG controller groups are analyzed. Finally, the effects of design parameters on ZG controller group are also investigated.

The statistical fingerprints left by median filtering can be a valuable clue for image forensics. However, these fingerprints may be maliciously erased by a forger. Recently, a tricky anti-forensic method has been proposed to remove median filtering traces by restoring images' pixel difference distribution. In this paper, we analyze the traces of this anti-forensic technique and propose a novel counter method. The experimental results show that our method could reveal this anti-forensics effectively at low computation load. According to our best knowledge, it's the first work on countering anti-forensics of median filtering.

It's well known that JPEG image compression can result in quantization artifacts and blocking artifacts. There are plenty of forensic techniques making use of image's compression fingerprints to verify digital images. However, when a forger exists, these methods are not reliable any more. One typical anti-forensic method is adding anti-forensic dither to DCT transform coefficients and erasing blocking artifacts to remove compression history. In this paper, we propose a new countering anti-forensic method based on estimating the noise added in the process of erasing blocking artifacts. The experimental results show that our method obtains an average detection accuracy of 98% on the UCID image database. Another advantage of our proposed method is that it has only one-dimensional feature and time-saving. Furthermore, we use the game theory to evaluate the performance of both sides, and identify the optimal strategies of both sides.

Sensor pattern noise (SPN) is recognized as a reliable device fingerprint for camera source identification (CSI). However, source identification method (source test) ignores whether the fingerprint is forged and anti-forensic techniques seldom consider traces they leave behind. Therefore, the performance of above techniques needs to be evaluated again by assuming the existence of both parties of a forensic investigator and an anti-forensic forger. In this paper, we propose a novel counter anti-forensic method based on noise level estimation to detect the possible forgery (forgery test). Furthermore, we evaluate the Nash equilibrium performance when investigator performs both source test and forgery test, and identify the optimal strategies of both parties with the game theory. The experimental results show that our proposed method can achieve good performance without collecting the candidate image set in the existing triangle test method especially when the false alarm rate is held low (e.g. Pfa

Although sensor pattern noise (SPN) is recognized as a reliable device fingerprint for camera source identification (CSI), this fingerprint could have been forged by anti-forensics. In order to evaluate the performance in the case of both forensic investigator and forger exist, we model this interplay as a camera source identification game. The mixed-strategy Nash equilibrium is introduced to solve this game. The Nash equilibrium receiver operating characteristic (ROC) curves are obtained experimentally. Through our analysis, we are able to determine under which case the CSI result is reliable.

With the rapid development of multimedia techniques, the video can easily be edited and tempered. To determine whether a video has been modified from its original recording has become a critical challenge. Thanks to the flexibility of H.264, we observe that different H.264 video parameters are used by various manufacturers. We propose a video capturing device identification approach by exploring the H.264 cues. Two main video dependent features are exploited: 1) H.264 encoding parameters which rely on motion-vector analysis, entropy coding, encoding quality and level, GOP information, etc. 2) The other is the video encapsulation formats. The former is to judge whether the video is taken by cellphone, while the latter is utilized to identify the video source. In the experimental part, we evaluate our proposed approach on our collected video dataset, and demonstrate the efficiency of our method.

Compression of encrypted data draws much attention in recent years due to the security concerns in a service oriented environment such as cloud computing. We propose a scalable lossy compression scheme for images having their pixel value encrypted with a standard stream cipher. The encrypted data are simply compressed by transmitting a uniformly sub sampled portion of the encrypted data and some bit-planes of another uniformly sub sampled portion of the encrypted data. With a proposed content adaptive interpolation prediction method with side information, at the receiver side, a decoder performs content adaptive interpolation based on the decrypted partial information, where the received bit-plane information serves as the side information that reflects the image edge information, making the image reconstruction more precise. When more bit-planes are transmitted, higher quality of the decompressed image can be achieved. The experimental results show that our proposed scheme achieves much better performance than the existing lossy compression scheme for pixel value encrypted images, and also similar performance as the state-of-the-art lossy compression for pixel permutation based encrypted images. In addition, our proposed scheme has the following advantages: at the decoder side, no computationally intensive iteration and no additional public orthogonal matrix is needed. It works well for both smooth and texture-rich images.

Print-scan resilient data hiding has not been extensively researched. This paper presents an efficient multi-bit blind watermarking scheme based on a novel Fourier log-polar mapping (LPM). The watermark resynchronization after print-scanning is efficiently solved by an embedded tracking pattern which cannot be removed by template removing attacks and is not detectable for a malicious part. Experimental results show that the proposed watermarking scheme has excellent robustness to print-scanning, cropping, geometric distortion and JPEG compression etc. The obtained success ratios of extraction 60 bits message without error from the combination attack of JPEG compressed with quality factor of 50-100 and then print- scanning were at least 95%.

Quality fluctuation has a major negative effect on perceptive video quality. In J. Lan et al., (2004), we derived accurate approximations in close-form for the highly nonlinear rate-distortion (R-D) and distortion-quantization (D-Q) relationships, all at the frame-level. Based on the two close forms, we can allocate the bit rate at the frame-level rather easily as far as a target distortion for each frame could be established. In J. Lan et al., (2004), a target distortion was set up for each frame based on a hypothesis that maintaining constant distortion over frames would boast video quality smoothing and extensive experiments showed the constant-distortion bit allocation (CDBA) scheme significantly outperformed the popular constant bit allocation (CBA) scheme in terms of delivered video quality. Maintaining constant distortion is no different from maintaining constant Peak-Signal-to-Noise-Ratio (PSNR). In scene changes, however, the picture energy often dramatically changes, producing significantly different Signal-to-Noise-Ratio (SNR) if constant distortion or constant PSNR is maintained. Although computationally more complex, SNR represents a more objective measure than PSNR in assessing picture/video quality. In the paper, an SNR-based bit allocation scheme is developed for video quality smoothing. The algorithm uses a single pass and attempts to maintain constant SNR at the frame level throughout the video sequence. Experimental results on all testing video sequences show that the proposed CSNRBA scheme provides smooth video quality in terms of natural color and sharp objects and silhouette significantly better than both the CBA and CDBA schemes

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.

Almost all the existing popular watermarking schemes model the watermarking channel noises as additive noise with zero-mean. However, our experiments show that this is not reasonable in the case of channel noise introduced by image filtering. This paper presents a new quantization-based watermarking scheme with enhanced robustness via adaptive receiving and turbo coding in addition to other measures. The present algorithm can successfully resist almost all the StirMark testing functions including both common signal processing and geometric distortions in StirMark 4.0 except for random distortion.