Acquiring authentic data in unattended wireless sensor networks.

An Unattended Wireless Sensor Network (UWSN) can be used in many applications to collect valuable data. Nevertheless, due to the unattended nature, the sensors could be compromised and the sensor readings would be maliciously altered so that the sink accepts the falsified sensor readings. Unfortunately, few attentions have been given to this authentication problem. Moreover, existing methods suffer from different kinds of DoS attacks such as Path-Based DoS (PDoS) and False Endorsement-based DoS (FEDoS) attacks. In this paper, a scheme, called AAD, is proposed to Acquire Authentic Data in UWSNs. We exploit the collaboration among sensors to address the authentication problem. With the proper design of the collaboration mechanism, AAD has superior resilience against sensor compromises, PDoS attack, and FEDoS attack. In addition, compared with prior works, AAD also has relatively low energy consumption. In particular, according to our simulation, in a network with 1,000 sensors, the energy consumed by AAD is lower than 30% of that consumed by the existing method, ExCo. The analysis and simulation are also conducted to demonstrate the superiority of the proposed AAD scheme over the existing methods.

Denoising and copy attacks resilient watermarking by exploiting prior knowledge at detector.

Watermarking with both oblivious detection and high robustness capabilities is still a challenging problem. In this paper, we tackle the aforementioned problem. One easy way to achieve blind detection is to use denoising for filtering out the hidden watermark, which can be utilized to create either a false positive (copy attack) or false negative (denoising and remodulation attack). Our basic design methodology is to exploit prior knowledge available at the detector side and then use it to design a "nonblind" embedder. We prove that the proposed scheme can resist two famous watermark estimation-based attacks, which have successfully cracked many existing watermarking schemes. False negative and false positive analyses are conducted to verify the performance of our scheme. The experimental results show that the new method is indeed powerful.

A new iterated two-band diffusion equation: theory and its application.

In this paper, we propose an iterated two-band filtering method to solve the selective image smoothing problem. We prove that a discrete computation step in an iterated nonlinear diffusion-based filtering algorithm is equivalent to a sequence of operations, including decomposition, regularization, and then reconstruction, in the proposed two-band filtering scheme. To correctly separate the high frequency components from the low frequency ones in the decomposition process, we adopt a dyadic wavelet-based approximation scheme. In the regularization process, we use a diffusivity function as a guide to retain useful data and suppress noises. Finally, the signal of the next stage, which is a "smoother" version of the signal at the previous stage, can be computed by reconstructing the decomposed low frequency component and the regularized high frequency component. Based on the proposed scheme, the smoothing operation can be applied to the correct targets. Experimental results show that our new approach is really efficient in noise removing.

Image feature extraction in encrypted domain with privacy-preserving SIFT.

Privacy has received considerable attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario where the server is resource-abundant, and is capable of finishing the designated tasks. It is envisioned that secure media applications with privacy preservation will be treated seriously. In view of the fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this paper is the first to target the importance of privacy-preserving SIFT (PPSIFT) and to address the problem of secure SIFT feature extraction and representation in the encrypted domain. As all of the operations in SIFT must be moved to the encrypted domain, we propose a privacy-preserving realization of the SIFT method based on homomorphic encryption. We show through the security analysis based on the discrete logarithm problem and RSA that PPSIFT is secure against ciphertext only attack and known plaintext attack. Experimental results obtained from different case studies demonstrate that the proposed homomorphic encryption-based privacy-preserving SIFT performs comparably to the original SIFT and that our method is useful in SIFT-based privacy-preserving applications.

Temporal frequency of flickering-distortion optimized video halftoning for electronic paper.

Video halftoning is a key technology for use in electronic paper (e-paper) or smart paper, which is an emerging display device that has received considerable attention recently. In this paper, a temporal frequency of flickering-distortion optimized video halftoning method is proposed. We first uncover three visual defects that conventional neighboring frame referencing-based video halftoning methods, due to their sequential changes of reference frames, will encounter. To deal with the problem, we then propose a reference frame update per GOP-based error diffusion video halftoning method based on a flickering sensitivity-based human visual model. To efficiently compromise between average temporal frequency of flickering (ATFoF) and visual quality, temporal frequency of flickering-distortion (TFoFD) is presented as a metric for video halftoning performance evaluation. Based on the proposed probability model of video halftoning, the TFoFD curve can be accurately estimated to optimize the tradeoff between quality and ATFoF before the video is halftoned. Our temporal frequency of flickering-distortion optimization strategy can also be applied to other video halftoning schemes for performance improvement. Experimental results and comparisons with known methods demonstrate the effectiveness of our video halftoning method.