Improved Lossless Data Hiding for JPEG Images Based on Histogram Modification

This paper proposes a lossless and high payload data hiding scheme for JPEG images by histogram modification.The most in JPEG bitstream consists of a sequence of VLCs(variable length codes)and the appended bits.Each VLC has a corresponding RLV(run/length value)to record the AC/DC coefficients.To achieve lossless data hiding with high payload,we shift the histogram of VLCs and modify the DHT segment to embed data.Since we sort the histogram of VLCs in descending order,the filesize expansion is limited.The paper’s key contribution includes:Lossless data hiding,less filesize expansion in identical pay-load and higher embedding efficiency.

FTG: Score-based black-box watermarking by fragile trigger generation for deep model integrity verification

Deep neural networks(DNNs)are widely used in real-world applications,thanks to their exceptional performance in image recognition.However,their vulnerability to attacks,such as Trojan and data poison,can compromise the integrity and stability of DNN applications.Therefore,it is crucial to verify the integrity of DNN models to ensure their security.Previous research on model watermarking for integrity detection has encountered the issue of overexposure of model parameters during embedding and extraction of the watermark.To address this problem,we propose a novel score-based black-box DNN fragile watermarking framework called fragile trigger generation(FTG).The FTG framework only requires the prediction probability distribution of the final output of the classifier during the watermarking process.It generates different fragile samples as the trigger,based on the classification prediction probability of the target classifier and a specified prediction probability mask to watermark it.Different prediction probability masks can promote the generation of fragile samples in corresponding distribution types.The whole watermarking process does not affect the performance of the target classifier.When verifying the watermarking information,the FTG only needs to compare the prediction results of the model on the samples with the previous label.As a result,the required model parameter information is reduced,and the FTG only needs a few samples to detect slight modifications in the model.Experimental results demonstrate the effectiveness of our proposed method and show its superiority over related work.The FTG framework provides a robust solution for verifying the integrity of DNN models,and its effectiveness in detecting slight modifications makes it a valuable tool for ensuring the security and stability of DNN applications.

Chronic heart failure:Role of the GP in management

The commonest cause of chronic heat failure in China is ischemic heart disease,followed by hypertension and valvular heart disease.Echocardiography is essential in establishing a diagnosis as well as helping to identify a cause and to monitor progress.Management includes nonpharmacological as well as pharmacological treatment,and self-care with careful monitoring of salt and fluid intake as well as regular weight measurement.Care planning and team-based care are essential in managing patients with chronic heart failure,who often have concurrent multimorbidity and are receiving polypharmacy.