Prior studies on Aspect-level Sentiment Classification (ALSC) emphasize modeling interrelationships among aspects and contexts but overlook the crucial role of aspects themselves as essential domain knowledge. To this...
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Various stakeholders, such as researchers, government agencies, businesses, and research laboratories require a large volume of reliable scientific research outcomes including research articles and patent data to supp...
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The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performan...
With the rapid development of the biomedical field, stem cell therapy has received widespread attention as an innovative therapy with great potential. However, privacy protection and secure sharing of stem cell testin...
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With the rapid development of mobile Internet and video applications, robust video watermarking technology has become a focal area of research for protecting and tracking intellectual property rights in digital media....
With the rapid development of mobile Internet and video applications, robust video watermarking technology has become a focal area of research for protecting and tracking intellectual property rights in digital media. An important characteristic of video is that it has both spatial and temporal properties. Previous studies in video watermarking have primarily focused on either spatial or temporal distortions and did not uniformly consider all types of video distortion, which restricts the robustness of video watermarking. In this paper, a novel robust blind video watermarking is proposed by exploring consistent spatio-temporal distortion and stable 3-D DCT coefficients. Our method achieves stronger robustness by uniformly treating the spatial and temporal distortions of the video. The properties of the stable 3-D DCT coefficients are mathematically proved, which makes the scheme generalizable. Extensive experiments have demonstrated that our method is resistant to video compression (H.264/AVC, H.265/HEVC) attacks, geometric attacks, and temporal domain attacks, and outperforms the current state-of-the-art video watermarking schemes.
The widespread adoption of smartphones has introduced new challenges to document copyright protection, prompting the emergence of Screen-Shooting Resilient Document Watermarking (SSRDW) technology. In recent years, un...
The widespread adoption of smartphones has introduced new challenges to document copyright protection, prompting the emergence of Screen-Shooting Resilient Document Watermarking (SSRDW) technology. In recent years, underpainting-based SSRDW techniques have proven to be highly effective. However, after careful study, we find that existing methods fail to simultaneously meet four essential criteria for SSRDW: high imperceptibility, strong robustness, adaptability to text processing, and high efficiency. In this paper, we introduce an enhanced underpainting-based SSRDW approach capable of satisfying all four requirements. Our approach enhances imperceptibility by employing underpainting embedding methods independent of text content. Additionally, we introduce a fast resynchronization mechanism to improve time efficiency. Furthermore, we propose an enhanced watermark extraction method that enhances robustness and enables watermark retrieval even in scenarios involving text processing. Extensive experimental validation underscores the superior performance of our enhanced SSRDW method.
Existing video watermarking embeds robust watermarks in each frame of the video for copyright protection and tracking. However, just as any content written on a blank paper is easily perceived, embedding watermarks in...
Existing video watermarking embeds robust watermarks in each frame of the video for copyright protection and tracking. However, just as any content written on a blank paper is easily perceived, embedding watermarks in the texture-poor frames impairs imperceptibility. Common geometric attacks such as scaling and rotation pose a significant challenge to the existing video watermarking. Image watermarking based on moments is robust against geometric attacks. However, moment-based watermarking is difficult to migrate to the video due to its lack of perceptual guarantee and high computational cost. In this paper, we propose an adaptive video watermarking scheme by exploring the relationship between moments and video textures, which can adaptively select texture-rich frames to embed watermarks for perceptual guarantee. Furthermore, we utilize the properties of moment calculation in videos to optimize efficiency. Extensive experiments show that the proposed method can achieve better imperceptibility than existing methods while maintaining strong robustness.
Song voice conversion tools have gained more and more popularity in the recent past. People have been uploading their self-made forgery songs on video websites, and these songs have been converted in timbre. However, ...
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ISBN:
(数字)9798350359312
ISBN:
(纸本)9798350359329
Song voice conversion tools have gained more and more popularity in the recent past. People have been uploading their self-made forgery songs on video websites, and these songs have been converted in timbre. However, singing voice conversion technology may cause copyright infringement of the songs. In order to protect the copyright of songs, the method of singing voice conversion detection needs to be investigated. We propose Rhythm and Pitch Aware Songs Conversion Detection (RPA-SCD), a dual-branch network for song voice conversion detection. RPA-SCD can predict forged song fragments through rhythm and pitch which are the global and local information of music. To evaluate the proposed method, we contribute a multilingual song conversion detection(MSCD) dataset. Our proposed model achieves the EER of 2.30% in the original domain of MSCD, which is lower than other benchmarks for speech forgery detection. The experiments show that our approach achieves state-of-the-art performance on the song conversion detection task. The MSCD dataset can be found at https://***/file/d/1rFsvMYihVtk81uFbL7UpyUEs-qBgsX6H/view?usp=drive_link. The code can be found at https://***/Samantha-Du/RPA-SDD.
Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation...
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Emergency resource management is a very important part of the public affairs emergency management system, once the emergence of public emergencies requires timely and efficient emergency rescue response, and rapid eme...
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