A blind digital image forensic method for detecting copy-paste forgery between JPEG images was proposed.Two copy-paste tampering scenarios were introduced at first:the tampered image was saved in an uncompressed forma...A blind digital image forensic method for detecting copy-paste forgery between JPEG images was proposed.Two copy-paste tampering scenarios were introduced at first:the tampered image was saved in an uncompressed format or in a JPEG compressed format.Then the proposed detection method was analyzed and simulated for all the cases of the two tampering scenarios.The tampered region is detected by computing the averaged sum of absolute difference(ASAD) images between the examined image and a resaved JPEG compressed image at different quality factors.The experimental results show the advantages of the proposed method:capability of detecting small and/or multiple tampered regions,simple computation,and hence fast speed in processing.展开更多
为了有效检测识别被篡改的古籍文字图像,提出一种可用于古籍文字图像篡改的检测识别模型MDAS-Net。首先在边缘监督分支中提出一种全新的特征融合方式即混合注意力块,以更好地提取图像中的多尺度目标信息;其次,针对边缘监督分支和噪声敏...为了有效检测识别被篡改的古籍文字图像,提出一种可用于古籍文字图像篡改的检测识别模型MDAS-Net。首先在边缘监督分支中提出一种全新的特征融合方式即混合注意力块,以更好地提取图像中的多尺度目标信息;其次,针对边缘监督分支和噪声敏感分支的特征融合设计一种特征传递模块E-2-N/N-2-E Help Block,促进2个分支间的信息交流,以得到更高质量的融合特征。为了验证模型的有效性,创建古籍图像篡改数据集,并联合篡改图像文本数据集(TTI)进行对比实验和消融实验。结果表明,MDAS-Net模型在古籍文字图像篡改区域检测效果良好,受试者工作特性曲线下的面积(AUC)达到了0.852,F_(1)值达到了0.784,并证明了MDAS-Net在检测古籍文字图像篡改方面的实用性。展开更多
基金Project(61172184) supported by the National Natural Science Foundation of ChinaProject(200902482) supported by China Postdoctoral Science Foundation Specially Funded ProjectProject(12JJ6062) supported by the Natural Science Foundation of Hunan Province,China
文摘A blind digital image forensic method for detecting copy-paste forgery between JPEG images was proposed.Two copy-paste tampering scenarios were introduced at first:the tampered image was saved in an uncompressed format or in a JPEG compressed format.Then the proposed detection method was analyzed and simulated for all the cases of the two tampering scenarios.The tampered region is detected by computing the averaged sum of absolute difference(ASAD) images between the examined image and a resaved JPEG compressed image at different quality factors.The experimental results show the advantages of the proposed method:capability of detecting small and/or multiple tampered regions,simple computation,and hence fast speed in processing.
文摘为了有效检测识别被篡改的古籍文字图像,提出一种可用于古籍文字图像篡改的检测识别模型MDAS-Net。首先在边缘监督分支中提出一种全新的特征融合方式即混合注意力块,以更好地提取图像中的多尺度目标信息;其次,针对边缘监督分支和噪声敏感分支的特征融合设计一种特征传递模块E-2-N/N-2-E Help Block,促进2个分支间的信息交流,以得到更高质量的融合特征。为了验证模型的有效性,创建古籍图像篡改数据集,并联合篡改图像文本数据集(TTI)进行对比实验和消融实验。结果表明,MDAS-Net模型在古籍文字图像篡改区域检测效果良好,受试者工作特性曲线下的面积(AUC)达到了0.852,F_(1)值达到了0.784,并证明了MDAS-Net在检测古籍文字图像篡改方面的实用性。
