Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,th...Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,the existing generative image steganography methods generally have good steganography performance,but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information extraction.Therefore,this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping rule.Firstly,the reference image is disentangled by a content and an attribute encoder to obtain content features and attribute features,respectively.Then,a mean mapping rule is introduced to map the binary secret information into a noise vector,conforming to the distribution of attribute features.This noise vector is input into the generator to produce the attribute transformed stego image with the content feature of the reference image.Additionally,we design an adversarial loss,a reconstruction loss,and an image diversity loss to train the proposed model.Experimental results demonstrate that the stego images generated by the proposed method are of high quality,with an average extraction accuracy of 99.4%for the hidden information.Furthermore,since the stego image has a uniform distribution similar to the attribute-transformed image without secret information,it effectively resists both subjective and objective steganalysis.展开更多
Personalized management of inflammatory bowel disease(IBD)is crucial due to the heterogeneity in disease presentation,variable therapeutic response,and the unpredictable nature of disease progression.Although artifici...Personalized management of inflammatory bowel disease(IBD)is crucial due to the heterogeneity in disease presentation,variable therapeutic response,and the unpredictable nature of disease progression.Although artificial intelligence(AI)and machine learning algorithms offer promising solutions by analyzing complex,multidimensional patient data,the“black-box”nature of many AI models limits their clinical adoption.Explainable AI(XAI)addresses this challenge by making data-driven predictions more transparent and clinically actionable.This mini-review focuses on recent advancements and clinical relevance of integrating XAI for personalized IBD management.We explore the importance of XAI in priori-tizing treatment and highlight how XAI techniques,such as feature-attribution explanations and interpretable model architectures,enhance transparency in AI models.In recent years,XAI models have been applied to diagnose IBD anomalies by prioritizing the predictive features for gastrointestinal bleeding and dietary intake patterns.Furthermore,studies have revealed that XAI application enhances IBD risk stratification and improves the prediction of drug efficacy and patient responses with high accuracy.By transforming opaque AI models into inter-pretable tools,XAI fosters clinician trust,supports personalized decision-making,and enables the safe deployment of AI systems in sensitive,individualized IBD care pathways.展开更多
基金supported in part by the National Natural Science Foundation of China(Nos.62202234,62401270)the China Postdoctoral Science Foundation(No.2023M741778)the Natural Science Foundation of Jiangsu Province(Nos.BK20240706,BK20240694).
文摘Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,the existing generative image steganography methods generally have good steganography performance,but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information extraction.Therefore,this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping rule.Firstly,the reference image is disentangled by a content and an attribute encoder to obtain content features and attribute features,respectively.Then,a mean mapping rule is introduced to map the binary secret information into a noise vector,conforming to the distribution of attribute features.This noise vector is input into the generator to produce the attribute transformed stego image with the content feature of the reference image.Additionally,we design an adversarial loss,a reconstruction loss,and an image diversity loss to train the proposed model.Experimental results demonstrate that the stego images generated by the proposed method are of high quality,with an average extraction accuracy of 99.4%for the hidden information.Furthermore,since the stego image has a uniform distribution similar to the attribute-transformed image without secret information,it effectively resists both subjective and objective steganalysis.
基金Supported by National Research Foundation of Korea,No.RS-2023-00237287。
文摘Personalized management of inflammatory bowel disease(IBD)is crucial due to the heterogeneity in disease presentation,variable therapeutic response,and the unpredictable nature of disease progression.Although artificial intelligence(AI)and machine learning algorithms offer promising solutions by analyzing complex,multidimensional patient data,the“black-box”nature of many AI models limits their clinical adoption.Explainable AI(XAI)addresses this challenge by making data-driven predictions more transparent and clinically actionable.This mini-review focuses on recent advancements and clinical relevance of integrating XAI for personalized IBD management.We explore the importance of XAI in priori-tizing treatment and highlight how XAI techniques,such as feature-attribution explanations and interpretable model architectures,enhance transparency in AI models.In recent years,XAI models have been applied to diagnose IBD anomalies by prioritizing the predictive features for gastrointestinal bleeding and dietary intake patterns.Furthermore,studies have revealed that XAI application enhances IBD risk stratification and improves the prediction of drug efficacy and patient responses with high accuracy.By transforming opaque AI models into inter-pretable tools,XAI fosters clinician trust,supports personalized decision-making,and enables the safe deployment of AI systems in sensitive,individualized IBD care pathways.
