Prof. Xiangui Kang Profile

Prof. Xiangui Kang

at Sun Yat-Sen Univ

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Publications (4)

Proceedings Article | 11 February 2011 Paper

Enhancing ROC performance of trustworthy camera source identification

Xiangui Kang, Yinxiang Li, Zhenhua Qu, Jiwu Huang

Proceedings Volume 7880, 788009 (2011) https://doi.org/10.1117/12.872595

KEYWORDS: Cameras, Sensors, Lithium, Digital cameras, CCD image sensors, Image sensors, Image compression, Optical filters, CCD cameras, Feature extraction

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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 512×512 pixels.

Proceedings Article | 27 February 2007 Paper

Fast and automatic watermark resynchronization based on Zernike moments

Xiangui Kang, Chunhui Liu, Wenjun Zeng, Jiwu Huang, Congbai Liu

Proceedings Volume 6505, 65050E (2007) https://doi.org/10.1117/12.703839

KEYWORDS: Digital watermarking, Distortion, Video, Optical correlators, Image compression, Pattern recognition, Fourier transforms, Image quality, Image processing, Video compression

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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.

Proceedings Article | 16 February 2006 Paper

Achieving non-ambiguity of quantization-based watermarking

Xiangui Kang, Yun Shi, Jiwu Huang

Proceedings Volume 6072, 60720F (2006) https://doi.org/10.1117/12.650876

KEYWORDS: Digital watermarking, Lithium, Quantization, Multimedia, Binary data, Discrete wavelet transforms, Targeting Task Performance metric, Electrochemical etching, Information security, Bismuth

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Proceedings Article | 24 June 2005 Paper

Digital watermarking based on multi-band wavelet and principal component analysis

Xiangui Kang, Wenjun Zeng, Jiwu Huang, Xinhua Zhuang, Yun-Qing Shi

Proceedings Volume 5960, 59603A (2005) https://doi.org/10.1117/12.631584

KEYWORDS: Digital watermarking, Wavelets, Principal component analysis, Discrete wavelet transforms, Transparency, Multimedia, Image compression, Image filtering, Binary data, Image quality

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