Wavelet-based compression is widely used to reduce image redundancy for efficiently storing and transmitting the data. Thus it is an important part in digital image forensics to trace the wavelet-based image compression history. The wavelet-based compression leaves comb-like quantization artifacts in the DWT (Discrete Wavelet Transform) histogram, which however can be disguised using a proper dithering operation. In this paper, standing on the forensic side, we study the joint histogram of DWT coefficients across different levels, whose pattern is robust across a wide range of natural uncompressed images but can be easily destroyed by the wavelet-based compression or the dithering operation used for anti-forensic purposes. By applying the Hough transform to the joint DWT histogram, we derive a 12-dimensional feature vector and a merged discriminating feature. Experimental results demonstrate the effectiveness of the proposed method for differentiating uncompressed images from (anti-forensic) wavelet-based compressed images.

Watermarking embeds information into a digital signal like audio, image, or video. Reversible image watermarking can restore the original image without any distortion after the hidden data is extracted. In this paper, we present a novel reversible watermarking scheme using an interpolation technique, which can embed a large amount of covert data into images with imperceptible modification. Different from previous watermarking schemes, we utilize the interpolation-error, the difference between interpolation value and corresponding pixel value, to embed bit ¿1¿ or ¿0¿ by expanding it additively or leaving it unchanged. Due to the slight modification of pixels, high image quality is preserved. Experimental results also demonstrate that the proposed scheme can provide greater payload capacity and higher image fidelity compared with other state-of-the-art schemes.

This paper proposes a reversible image watermarking scheme of low distortion and relatively large capacity, wherein prediction-errors are modified at most by 1 to embed secret bits. Different from most existing predictors, where only a partial prediction context is available, we provide a full context for every pixel in our watermarking scheme. Predictors operating on full contexts are preciser and thus produce smaller prediction-errors, which are more favorable for data embedding. Experimental results also validate that the proposed scheme can achieve high image fidelity while providing relatively large capacity.

The location awareness is a crucial foundation for perceptions of the surroundings in the smart environment. Radio frequency identification (RFID), as one of the most promising technologies, plays a more important role in the indoor location awareness. This paper surveys current RFID-based locating research and discusses the problem that is brought by the tag's diversity derived from different manufacturer types and different used-time of built-in battery. We present the algorithm named RFDiffFreeLoc to improve the location precision by eliminating the dissimilarity among tags. In the stimulation experiments, we analyze the impact of noise on performance and contrast our algorithm with the existing LANDMARC algorithm. The simulation performances show that our algorithm is feasible via two metrics: the mean error and cumulative error distribution. The results indicate that RFDiffFreeLoc significantly increases the locating accuracy: when the space between the reference tags is 1 m, the mean error drops 0.076–0.344 m according to various noise conditions. Furthermore, a prototype system named RFHome is deployed for validating the algorithm in the actual home environment. The practical experimental results demonstrate that our algorithm is more effective than previous LANDMARC algorithm.