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抗强剪切和涂抹攻击零水印算法 被引量:1
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作者 何冰 袁卫 苏变玲 《计算机应用与软件》 CSCD 北大核心 2013年第6期150-153,208,共5页
为了增强现有水印算法抵抗存在信息量丢失的各种攻击(如剪切、涂抹、行列移除等),提出一种基于NMF(Non-negative factorization)和Radon变换不变矩抗强剪切和涂抹攻击零水印算法。算法首先将原始图像V矩阵进行非负矩阵分解(NMF)得到基矩... 为了增强现有水印算法抵抗存在信息量丢失的各种攻击(如剪切、涂抹、行列移除等),提出一种基于NMF(Non-negative factorization)和Radon变换不变矩抗强剪切和涂抹攻击零水印算法。算法首先将原始图像V矩阵进行非负矩阵分解(NMF)得到基矩阵W和系数矩阵H。由NMF部分感知全局的特性可知,利用部分V矩阵图像信息和对应的系数矩阵H可以重构出完整的W矩阵;然后计算非负矩阵分解后W矩阵的Radon变换不变矩,最后利用有限个Radon变换不变矩来设计和构建零水印信息。实验结果表明:当剪切和涂抹的面积达到87.5%时,水印检测正确率为100%,同时对于加噪、滤波、JPEG压缩等攻击,该算法也具有良好的鲁棒性。 展开更多
关键词 非负矩阵分解 零水印 鲁棒性 Radon变换不变矩
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Improved YOLOv7 Algorithm for Floating Waste Detection Based on GFPN and Long-Range Attention Mechanism
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作者 PENG Cheng HE Bing +1 位作者 XI Wenqiang LIN Guancheng 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2024年第4期338-348,共11页
Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus result... Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus resulting in a degradation of detection performance.In order to tackle these challenges,a floating waste detection algorithm based on YOLOv7 is proposed,which combines the improved GFPN(Generalized Feature Pyramid Network)and a long-range attention mechanism.Firstly,we import the improved GFPN to replace the Neck of YOLOv7,thus providing more effective information transmission that can scale into deeper networks.Secondly,the convolution-based and hardware-friendly long-range attention mechanism is introduced,allowing the algorithm to rapidly generate an attention map with a global receptive field.Finally,the algorithm adopts the WiseIoU optimization loss function to achieve adaptive gradient gain allocation and alleviate the negative impact of low-quality samples on the gradient.The simulation results reveal that the proposed algorithm has achieved a favorable average accuracy of 86.3%in real-time scene detection tasks.This marks a significant enhancement of approximately 6.3%compared with the baseline,indicating the algorithm's good performance in floating waste detection. 展开更多
关键词 floating waste detection YOLOv7 GFPN(Generalized Feature Pyramid Network) long-range attention
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