Implementation of Adaptive Unsharp Masking as a pre-filtering method for watermark detection and extraction

Digital watermarking has been one of the focal points of research interests in order to provide multimedia security in the last decade. Watermark data, belonging to the user, are embedded on an original work such as text, audio, image, and video and thus, product ownership can be proved. Various robust watermarking algorithms have been developed in order to extract/detect the watermark against such attacks. Although watermarking algorithms in the transform domain differ from others by different combinations of transform techniques, it is difficult to decide on an algorithm for a specific application. Therefore, instead of developing a new watermarking algorithm with different combinations of transform techniques, we propose a novel and effective watermark extraction and detection method by pre-filtering, namely Adaptive Unsharp Masking (AUM). In spite of the fact that Unsharp Masking (UM) based pre-filtering is used for watermark extraction/detection in the literature by causing the details of the watermarked image become more manifest, effectiveness of UM may decrease in some cases of attacks. In this study, AUM has been proposed for pre-filtering as a solution to the disadvantages of UM. Experimental results show that AUM performs better up to 11\% in objective quality metrics than that of the results when pre-filtering is not used. Moreover; AUM proposed for pre-filtering in the transform domain image watermarking is as effective as that of used in image enhancement and can be applied in an algorithm-independent way for pre-filtering in transform domain image watermarking

A HIGH SPEED VLSI ARCHITECTURE FOR DIGITAL SPEECH WATERMARKING WITH COMPRESSION

The need to provide a copy right protection on digital watermarking to multimedia data like speech, image or video is rapidly increasing with an intensification in the application in these areas. Digital watermarking has received a lot of attention in the past few years. A hardware system based solely on DSP processors are fast but may require more area, cost or power if the target application requires a large amount of parallel processing. An FPGA co-processor can provide as many as 550 parallel multiply and accumulate operations on a single device, but FPGAs excel at processing large amounts of data in parallel, as they are not optimized as processors for tasks such as periodic coefficient updates, decision- making control tasks. Combination of both the FPGA and DSP processor delivers an attractive solution for a wide range of applications. A hardware implementation of digital speech watermarking combined with speech compression, encryption on heterogeneous platform is made in this paper. It is observed that the proposed architecture is able to attain high speed while utilizing optimal resources in terms of area

DeAR: A Deep-learning-based Audio Re-recording Resilient Watermarking

Audio watermarking is widely used for leaking source tracing. The robustness of the watermark determines the traceability of the algorithm. With the development of digital technology, audio re-recording (AR) has become an efficient and covert means to steal secrets. AR process could drastically destroy the watermark signal while preserving the original information. This puts forward a new requirement for audio watermarking at this stage, that is, to be robust to AR distortions. Unfortunately, none of the existing algorithms can effectively resist AR attacks due to the complexity of the AR process. To address this limitation, this paper proposes DeAR, a deep-learning-based audio re-recording resistant watermarking. Inspired by DNN-based image watermarking, we pioneer a deep learning framework for audio carriers, based on which the watermark signal can be effectively embedded and extracted. Meanwhile, in order to resist the AR attack, we delicately analyze the distortions that occurred in the AR process and design the corresponding distortion layer to cooperate with the proposed watermarking framework. Extensive experiments show that the proposed algorithm can resist not only common electronic channel distortions but also AR distortions. Under the premise of high-quality embedding (SNR=25.86dB), in the case of a common re-recording distance (20cm), the algorithm can effectively achieve an average bit recovery accuracy of 98.55%.Comment: Accepted by AAAI202

Spread Spectrum Based High Embedding Capacity Watermarking Method for Audio Signals

Audio watermarking is a promising technology for copyright protection of audio data. Built upon the concept of spread spectrum (SS), many SS-based audio watermarking methods have been developed, where a pseudonoise (PN) sequence is usually used to introduce security. A major drawback of the existing SS-based audio watermarking methods is their low embedding capacity. In this paper, we propose a new SS-based audio watermarking method which possesses much higher embedding capacity while ensuring satisfactory imperceptibility and robustness. The high embedding capacity is achieved through a set of mechanisms: embedding multiple watermark bits in one audio segment, reducing host signal interference on watermark extraction, and adaptively adjusting PN sequence amplitude in watermark embedding based on the property of audio segments. The effectiveness of the proposed audio watermarking method is demonstrated by simulation examples