Content-based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the sa...Content-based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the same video. In this paper, the “natural parts” (coarse scales) of the Shearlet coefficients are used to generate robust video fingerprints for content-based video copy detection applications. The proposed Shearlet-based video fingerprint (SBVF) is constructed by the Shearlet coefficients in Scale 1 (lowest coarse scale) for revealing the spatial features and Scale 2 (second lowest coarse scale) for revealing the directional features. To achieve spatiotemporal natural, the proposed SBVF is applied to Temporal Informative Representative Image (TIRI) of the video sequences for final fingerprints generation. A TIRI-SBVF based CBCD system is constructed with use of Invert Index File (IIF) hash searching approach for performance evaluation and comparison using TRECVID 2010 dataset. Common attacks are imposed in the queries such as luminance attacks (luminance change, salt and pepper noise, Gaussian noise, text insertion);geometry attacks (letter box and rotation);and temporal attacks (dropping frame, time shifting). The experimental results demonstrate that the proposed TIRI-SBVF fingerprinting algorithm is robust on CBCD applications on most of the attacks. It can achieve an average F1 score of about 0.99, less than 0.01% of false positive rate (FPR) and 97% accuracy of localization.展开更多
加密视频识别是网络安全和网络管理领域亟待解决的问题,已有的方法是将视频的加密传输指纹与视频指纹库中的视频指纹进行匹配,从而识别出加密传输的视频.现有工作主要集中在匹配识别算法的研究上,但是没有专门针对待匹配数据源的研究,...加密视频识别是网络安全和网络管理领域亟待解决的问题,已有的方法是将视频的加密传输指纹与视频指纹库中的视频指纹进行匹配,从而识别出加密传输的视频.现有工作主要集中在匹配识别算法的研究上,但是没有专门针对待匹配数据源的研究,也缺少在大型视频指纹库里对这些算法的查准率和假阳率指标的分析,由此造成现有成果的实用性不能保证.针对这一问题,首先分析使用安全传输层协议加密的应用数据单元(application data unit,简称ADU)密文长度相对明文长度发生漂移的原因,首次将HTTP头部特征和TLS片段特征作为ADU长度复原的拟合特征,提出了一种对加密ADU指纹精准复原方法HHTF,并将其应用于加密视频识别.基于真实Facebook视频模拟构建了20万级的大型指纹库.从理论上推导并计算出:只需已有方法十分之一的ADU数目,在该指纹库中视频识别准确率、查准率、查全率达到100%,假阳率达到0.在模拟大型视频指纹库中的实验结果与理论推导结果一致.HHTF方法的应用,使得在大规模视频指纹库场景中识别加密传输的视频成为可能,具有很强的实用性和应用价值.展开更多
文摘Content-based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the same video. In this paper, the “natural parts” (coarse scales) of the Shearlet coefficients are used to generate robust video fingerprints for content-based video copy detection applications. The proposed Shearlet-based video fingerprint (SBVF) is constructed by the Shearlet coefficients in Scale 1 (lowest coarse scale) for revealing the spatial features and Scale 2 (second lowest coarse scale) for revealing the directional features. To achieve spatiotemporal natural, the proposed SBVF is applied to Temporal Informative Representative Image (TIRI) of the video sequences for final fingerprints generation. A TIRI-SBVF based CBCD system is constructed with use of Invert Index File (IIF) hash searching approach for performance evaluation and comparison using TRECVID 2010 dataset. Common attacks are imposed in the queries such as luminance attacks (luminance change, salt and pepper noise, Gaussian noise, text insertion);geometry attacks (letter box and rotation);and temporal attacks (dropping frame, time shifting). The experimental results demonstrate that the proposed TIRI-SBVF fingerprinting algorithm is robust on CBCD applications on most of the attacks. It can achieve an average F1 score of about 0.99, less than 0.01% of false positive rate (FPR) and 97% accuracy of localization.
文摘加密视频识别是网络安全和网络管理领域亟待解决的问题,已有的方法是将视频的加密传输指纹与视频指纹库中的视频指纹进行匹配,从而识别出加密传输的视频.现有工作主要集中在匹配识别算法的研究上,但是没有专门针对待匹配数据源的研究,也缺少在大型视频指纹库里对这些算法的查准率和假阳率指标的分析,由此造成现有成果的实用性不能保证.针对这一问题,首先分析使用安全传输层协议加密的应用数据单元(application data unit,简称ADU)密文长度相对明文长度发生漂移的原因,首次将HTTP头部特征和TLS片段特征作为ADU长度复原的拟合特征,提出了一种对加密ADU指纹精准复原方法HHTF,并将其应用于加密视频识别.基于真实Facebook视频模拟构建了20万级的大型指纹库.从理论上推导并计算出:只需已有方法十分之一的ADU数目,在该指纹库中视频识别准确率、查准率、查全率达到100%,假阳率达到0.在模拟大型视频指纹库中的实验结果与理论推导结果一致.HHTF方法的应用,使得在大规模视频指纹库场景中识别加密传输的视频成为可能,具有很强的实用性和应用价值.
