The text watermarking is a feasible method to protect the copyright from being copied and tampered. In this paper, a text zero-watermarking algorithm is proposed based on the connection between the Chinese characters ...The text watermarking is a feasible method to protect the copyright from being copied and tampered. In this paper, a text zero-watermarking algorithm is proposed based on the connection between the Chinese characters and the Chinese phonetic alphabets. According to the predefined interval threshold, the proposed algorithm extracts the characteristics of the text content by valuing on the basis of the custom of Chinese phonetic alphabets. After being chaotic transformed, the algorithm combines the text characteristics with the embedded watermarking information in the Chinese text. The experimental results show that the watermarking＇s capability of preventing tampering is up to 0.1%, which demonstrates the strong robustness and resistance to aggressive behavior of the algorithm.展开更多
Privacy-sensitive data encounter immense security and usability challenges in processing,analyzing,and sharing.Meanwhile,traditional privacy data desensitization methods suffer from issues such as poor quality and low...Privacy-sensitive data encounter immense security and usability challenges in processing,analyzing,and sharing.Meanwhile,traditional privacy data desensitization methods suffer from issues such as poor quality and low usability after desensitization.Therefore,a text data desensitization model that combines Transformer and Wasserstein Text convolutional Generative Adversarial Network(Trans-WTGAN)is proposed.Transformer as the generator and its self-attention mechanism can handle long-range dependencies,enabling the generated of higher-quality text;Text Convolutional Neural Network(TextCNN)integrates the idea of Wasserstein as the discriminator to enhance the stability of model training;and the strategy gradient scheme of reinforcement learning is employed.Reinforcement learning utilizes the policy gradient scheme as the updating method of generator parameters,ensuring the generated data retains the original key features and maintains a certain level of usability.The experimental results indicate that the proposed model scheme holds a greater advantage over existing methods in terms of text quality and structural consistency,can guarantee the desensitization effect,and ensures the usability of the privacy-sensitive data to a certain extent after desensitization,facilitates the simulation of the development environment for the use of real data and the analysis and sharing of data.展开更多
Hierarchical multi-granularity image classification is a challenging task that aims to tag each given image with multiple granularity labels simultaneously.Existing methods tend to overlook that different image region...Hierarchical multi-granularity image classification is a challenging task that aims to tag each given image with multiple granularity labels simultaneously.Existing methods tend to overlook that different image regions contribute differently to label prediction at different granularities,and also insufficiently consider relationships between the hierarchical multi-granularity labels.We introduce a sequence-to-sequence mechanism to overcome these two problems and propose a multi-granularity sequence generation(MGSG)approach for the hierarchical multi-granularity image classification task.Specifically,we introduce a transformer architecture to encode the image into visual representation sequences.Next,we traverse the taxonomic tree and organize the multi-granularity labels into sequences,and vectorize them and add positional information.The proposed multi-granularity sequence generation method builds a decoder that takes visual representation sequences and semantic label embedding as inputs,and outputs the predicted multi-granularity label sequence.The decoder models dependencies and correlations between multi-granularity labels through a masked multi-head self-attention mechanism,and relates visual information to the semantic label information through a crossmodality attention mechanism.In this way,the proposed method preserves the relationships between labels at different granularity levels and takes into account the influence of different image regions on labels with different granularities.Evaluations on six public benchmarks qualitatively and quantitatively demonstrate the advantages of the proposed method.Our project is available at https://github.com/liuxindazz/mgs.展开更多
基金Supported by the National Natural Science Foundation of China(91112003)Youth Foundation(31541311307)
文摘The text watermarking is a feasible method to protect the copyright from being copied and tampered. In this paper, a text zero-watermarking algorithm is proposed based on the connection between the Chinese characters and the Chinese phonetic alphabets. According to the predefined interval threshold, the proposed algorithm extracts the characteristics of the text content by valuing on the basis of the custom of Chinese phonetic alphabets. After being chaotic transformed, the algorithm combines the text characteristics with the embedded watermarking information in the Chinese text. The experimental results show that the watermarking＇s capability of preventing tampering is up to 0.1%, which demonstrates the strong robustness and resistance to aggressive behavior of the algorithm.
基金supported by the National Natural Science Foundation of China(No.62262013).
文摘Privacy-sensitive data encounter immense security and usability challenges in processing,analyzing,and sharing.Meanwhile,traditional privacy data desensitization methods suffer from issues such as poor quality and low usability after desensitization.Therefore,a text data desensitization model that combines Transformer and Wasserstein Text convolutional Generative Adversarial Network(Trans-WTGAN)is proposed.Transformer as the generator and its self-attention mechanism can handle long-range dependencies,enabling the generated of higher-quality text;Text Convolutional Neural Network(TextCNN)integrates the idea of Wasserstein as the discriminator to enhance the stability of model training;and the strategy gradient scheme of reinforcement learning is employed.Reinforcement learning utilizes the policy gradient scheme as the updating method of generator parameters,ensuring the generated data retains the original key features and maintains a certain level of usability.The experimental results indicate that the proposed model scheme holds a greater advantage over existing methods in terms of text quality and structural consistency,can guarantee the desensitization effect,and ensures the usability of the privacy-sensitive data to a certain extent after desensitization,facilitates the simulation of the development environment for the use of real data and the analysis and sharing of data.
基金supported by National Key R&D Program of China(2019YFC1521102)the National Natural Science Foundation of China(61932003)Beijing Science and Technology Plan(Z221100007722004).
文摘Hierarchical multi-granularity image classification is a challenging task that aims to tag each given image with multiple granularity labels simultaneously.Existing methods tend to overlook that different image regions contribute differently to label prediction at different granularities,and also insufficiently consider relationships between the hierarchical multi-granularity labels.We introduce a sequence-to-sequence mechanism to overcome these two problems and propose a multi-granularity sequence generation(MGSG)approach for the hierarchical multi-granularity image classification task.Specifically,we introduce a transformer architecture to encode the image into visual representation sequences.Next,we traverse the taxonomic tree and organize the multi-granularity labels into sequences,and vectorize them and add positional information.The proposed multi-granularity sequence generation method builds a decoder that takes visual representation sequences and semantic label embedding as inputs,and outputs the predicted multi-granularity label sequence.The decoder models dependencies and correlations between multi-granularity labels through a masked multi-head self-attention mechanism,and relates visual information to the semantic label information through a crossmodality attention mechanism.In this way,the proposed method preserves the relationships between labels at different granularity levels and takes into account the influence of different image regions on labels with different granularities.Evaluations on six public benchmarks qualitatively and quantitatively demonstrate the advantages of the proposed method.Our project is available at https://github.com/liuxindazz/mgs.
