Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability.However,image watermarking algorithms are weak against hybrid attacks,especially geometric attacks,such as cr...Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability.However,image watermarking algorithms are weak against hybrid attacks,especially geometric attacks,such as cropping attacks,rotation attacks,etc.We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further.First,to extract more sparse and stable interest points,we use the Superpoint algorithm for generation and design two steps to perform the screening procedure.We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability.The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework.Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks.We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks.Through extensive experimental results,we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image.Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.展开更多
Bionic adhesives with tip-expanded microstructural arrays have attracted considerable interest owing to their high adhesive performance at low preloads.Their mainstream manufacturing method is molding.Due to most mold...Bionic adhesives with tip-expanded microstructural arrays have attracted considerable interest owing to their high adhesive performance at low preloads.Their mainstream manufacturing method is molding.Due to most molds are made of silicon or silicon-based soft templates,and have poor wear resistant or vulnerability to high temperature,limiting their use in large-scale manufacturing.Nickel is widely used as an embossing mold in the micro/nano-imprint industrial process owing to its good mechanical properties.However,the processing of metal molds for the fabrication of tip-expanded microstructural arrays is extremely challenging.In this study,using electrodeposition techniques,the shape of the micropores is modified to obtain end-controlled pores.The effect of the non-uniformity of the electric field on the microporous morphology in the electrodeposition process is systematically investigated.Furthermore,the mechanism of non-uniformity evolution of the microporous morphology is revealed.The optimized microporous metal array is used as a mold to investigate the cavity evolution laws of the elastic cushions under pre-load during the manufacturing process.As a result,typical bionic adhesives with tip-expansion are obtained.Moreover,corresponding adhesion mechanics are analyzed.The results show that electrochemical modifications have broad application prospects in the fabrication of tip-expanded microstructures,providing a new method for the large-scale fabrication of bionic adhesives based on metal molds.展开更多
文摘Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability.However,image watermarking algorithms are weak against hybrid attacks,especially geometric attacks,such as cropping attacks,rotation attacks,etc.We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further.First,to extract more sparse and stable interest points,we use the Superpoint algorithm for generation and design two steps to perform the screening procedure.We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability.The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework.Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks.We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks.Through extensive experimental results,we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image.Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.
基金the Natural Science Foundation of Jiangsu Province of China(No.BK20170796)the foundation of‘‘Jiangsu Provincial Key Laboratory of Bionic Functional Materials”of China(No.NJ2020026)+1 种基金the foundation of National Defense Key Laboratory of China(No.6142004190204)the National Natural Science Foundation of China(No.52075249)。
文摘Bionic adhesives with tip-expanded microstructural arrays have attracted considerable interest owing to their high adhesive performance at low preloads.Their mainstream manufacturing method is molding.Due to most molds are made of silicon or silicon-based soft templates,and have poor wear resistant or vulnerability to high temperature,limiting their use in large-scale manufacturing.Nickel is widely used as an embossing mold in the micro/nano-imprint industrial process owing to its good mechanical properties.However,the processing of metal molds for the fabrication of tip-expanded microstructural arrays is extremely challenging.In this study,using electrodeposition techniques,the shape of the micropores is modified to obtain end-controlled pores.The effect of the non-uniformity of the electric field on the microporous morphology in the electrodeposition process is systematically investigated.Furthermore,the mechanism of non-uniformity evolution of the microporous morphology is revealed.The optimized microporous metal array is used as a mold to investigate the cavity evolution laws of the elastic cushions under pre-load during the manufacturing process.As a result,typical bionic adhesives with tip-expansion are obtained.Moreover,corresponding adhesion mechanics are analyzed.The results show that electrochemical modifications have broad application prospects in the fabrication of tip-expanded microstructures,providing a new method for the large-scale fabrication of bionic adhesives based on metal molds.
