With the popularity of smartphones,it is often easy to maliciously leak important information by taking pictures of the phone.Robust watermarking that can resist screen photography can achieve the protection of inform...With the popularity of smartphones,it is often easy to maliciously leak important information by taking pictures of the phone.Robust watermarking that can resist screen photography can achieve the protection of information.Since the screen photo process can cause some irreversible distortion,the currently available screen photo watermarks do not consider the image content well and the visual quality is not very high.Therefore,this paper proposes a new screen-photography robust watermark.In terms of embedding region selection,the intensity-based Scale-invariant feature transform(SIFT)algorithm used for the construction of feature regions based on the density of feature points,which can make it more focused on the key content of the image;in terms of embedding strength,the Just noticeable difference(JND)model is applied to limit the intensity of the watermark embedding according to the luminance and texture of the picture to balance robustness and invisibility;after embedding watermark,the coefficients in the neighborhood are again adjusted with optimal constraints to improve the accuracy of watermark extraction.After experiments,it is shown that the method we proposed can improve the correct rate of watermark extraction,the quality of the visual aspect of the watermarked picture is also improved.展开更多
In recent years,academic misconduct has been frequently exposed by the media,with serious impacts on the academic community.Current research on academic misconduct focuses mainly on detecting plagiarism in article con...In recent years,academic misconduct has been frequently exposed by the media,with serious impacts on the academic community.Current research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a manuscript.For the most part,these techniques can only detect cases of textual plagiarism,which means that potential culprits can easily avoid discovery through clever editing and alterations of text content.In this paper,we propose an academic misconduct detection method based on scholars’submission behaviors.The model can effectively capture the atypical behavioral approach and operation of the author.As such,it is able to detect various types of misconduct,thereby improving the accuracy of detection when combined with a text content analysis.The model learns by forming a dual network group that processes text features and user behavior features to detect potential academic misconduct.First,the effect of scholars’behavioral features on the model are considered and analyzed.Second,the Synthetic Minority Oversampling Technique(SMOTE)is applied to address the problem of imbalanced samples of positive and negative classes among contributing scholars.Finally,the text features of the papers are combined with the scholars’behavioral data to improve recognition precision.Experimental results on the imbalanced dataset demonstrate that our model has a highly satisfactory performance in terms of accuracy and recall.展开更多
基金This work is supported by the National Key R&D Program of China under grant 2018YFB1003205by the National Natural Science Foundation of China under grant U1836208,U1836110+1 种基金by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘With the popularity of smartphones,it is often easy to maliciously leak important information by taking pictures of the phone.Robust watermarking that can resist screen photography can achieve the protection of information.Since the screen photo process can cause some irreversible distortion,the currently available screen photo watermarks do not consider the image content well and the visual quality is not very high.Therefore,this paper proposes a new screen-photography robust watermark.In terms of embedding region selection,the intensity-based Scale-invariant feature transform(SIFT)algorithm used for the construction of feature regions based on the density of feature points,which can make it more focused on the key content of the image;in terms of embedding strength,the Just noticeable difference(JND)model is applied to limit the intensity of the watermark embedding according to the luminance and texture of the picture to balance robustness and invisibility;after embedding watermark,the coefficients in the neighborhood are again adjusted with optimal constraints to improve the accuracy of watermark extraction.After experiments,it is shown that the method we proposed can improve the correct rate of watermark extraction,the quality of the visual aspect of the watermarked picture is also improved.
基金This work is supported by the National Key R&D Program of China under grant 2018YFB1003205by the National Natural Science Foundation of China under grants U1836208 and U1836110+1 种基金by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundand by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘In recent years,academic misconduct has been frequently exposed by the media,with serious impacts on the academic community.Current research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a manuscript.For the most part,these techniques can only detect cases of textual plagiarism,which means that potential culprits can easily avoid discovery through clever editing and alterations of text content.In this paper,we propose an academic misconduct detection method based on scholars’submission behaviors.The model can effectively capture the atypical behavioral approach and operation of the author.As such,it is able to detect various types of misconduct,thereby improving the accuracy of detection when combined with a text content analysis.The model learns by forming a dual network group that processes text features and user behavior features to detect potential academic misconduct.First,the effect of scholars’behavioral features on the model are considered and analyzed.Second,the Synthetic Minority Oversampling Technique(SMOTE)is applied to address the problem of imbalanced samples of positive and negative classes among contributing scholars.Finally,the text features of the papers are combined with the scholars’behavioral data to improve recognition precision.Experimental results on the imbalanced dataset demonstrate that our model has a highly satisfactory performance in terms of accuracy and recall.
