In this paper, an authentication algorithm of audio aggregate based on the (k, n/k=n) method is proposed, which can implement integrity authentication for audio aggregation. Watermark was distributed into n shares by means of the (k, n) method, and these n shares were embedded into each audio carrier of aggregation. Because the watermark is embedded into the whole audio aggregation, the watermark is only visible in the aggregation while it is not meaningful in each audio. The k sharing watermarks were recovered from k audio carriers in aggregation, and then the watermark was constructed by the (k, n) method. In the paper, k is set as n. The recovered watermark is relative with all detected audio signals in aggregation, which improved the fragility of the proposed algorithm. Experimental results show that the proposed algorithm has good imperceptibility, strong fragility and low computational complexity.
Copy Detection Pattern (CDP) is a high-density random noise-alike image that exhibits a different noise pattern after physical copying, and is thus treated as a promising anti-counterfeiting solution. However, CDP cannot convey any message, and it is often used in combination with additional carriers, such as QR codes. In this letter, we take the first step towards extending CDP with watermarking functionality. Specifically, we devise a scheme called Mixed-bit Sampling Graphic (MSG), which could realize invisible watermarking and anti-counterfeiting simultaneously. Compared with conventional CDP, the noise pattern generation of MSG is controlled by the portions of sampling over two bit templates. We formulate this mixed-bit sampling process as an optimization problem and solve it using a block coordinate descent sampling algorithm. Experimental results validate that the proposed MSG can effectively communicate watermark bits while retaining the anti-counterfeiting capability of CDP
A novel audio information hiding technology combined with ICA (independent component analysis) and QIM (quantized index modulation) is proposed. ICA method is applied to audio signal processing to obtain the statistical independent sources as hiding channels. As ICA processing is sensitive to the only dividing matrix, security is guaranteed. A piece of meaningful speech signal is hidden in the carrier audio. Through analytic comparisons and experimental results, this algorithm is found to have a larger hiding capacity and a lower distortion to carrier audio, and also robust against a variety of common signal processing manipulations
Authenticity is one of the most important evaluation factors of images for photography competitions or journalism. Unusual compression history of an image often implies the illicit intent of its author. Our work aims at distinguishing real uncompressed images from fake uncompressed images that are saved in uncompressed formats but have been previously compressed. To detect the potential image JPEG compression, we analyze the JPEG compression artifacts based on the tetrolet covering, which corresponds to the local image geometrical structure. Since the compression can alter the structure information, the tetrolet covering indexes may be changed if a compression is performed on the test image. Such changes can provide valuable clues about the image compression history. To be specific, the test image is first compressed with different quality factors to generate a set of temporary images. Then, the test image is compared with each temporary image block-by-block to investigate whether the tetrolet covering index of each 4×4 block is different between them. The percentages of the changed tetrolet covering indexes corresponding to the quality factors (from low to high) are computed and used to form the p-curve, the local minimum of which may indicate the potential compression. Our experimental results demonstrate the advantage of our method to detect JPEG compressions of high quality, even the highest quality factors such as 98, 99, or 100 of the standard JPEG compression, from uncompressed-format images. At the same time, our detection algorithm can accurately identify the corresponding compression quality factor.
Psychoacoustic model involves core technology in perceptual audio encoding,and it directly affects the encoding quality and compress radio.This paper first introduces the basic principles of psychoacoustic, mainly including the absolute threshold of hearing,masking effect and critical bands.Then,combining with mathematical model of psychoacoustic,the algorithmic process in accordance with mp3 standard procedure modules is analyzed.In the end,the pre-echo producing mechanism and reducing method in mp3 encoding are described with a given number of experiments.
For the morphology edge detector has a higher algorithm complexity and the contradiction between noise restraining and algorithm accuracy, a new edge detection algorithm based on morphology is proposed in this paper. First, the gray value in the structural elements will be sorted. Second, using the maximum value minus the minimum and the second largest value minus the second smallest value, and so on, getting the new values, selecting the appropriate value instead of the original structural elements point; Last, using mean and standard deviation to realize non-edge suppression and edge enhancement. The experimental results showed that the operation could effectively reduce the complexity of the algorithm, and the operation we presented were more effective than other classical and ordinary morphological operations of edge detection on noise restraining and retaining the image details.
In order to authenticate the integrity of audio aggregation, a fragile watermarking algorithm based on vector quantization has proposed which combining with Huffman coding. Put the output index into Huffman coding after obtaining the index's probability distribution by quantifying index address table. Then use the classical method of cryptography to complete zero-watermark embedding. The experimental results show that the proposed algorithm has good imperceptibility and high security. It has strong vulnerability for audio attacks, and can authenticate the integrity of audio aggregation.
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