A novel video data authentication model based on digital video watermarking and MAC （message authentication code） in multicast protocol is proposed in this paper, The digital watermarking which composes of the MAC o...A novel video data authentication model based on digital video watermarking and MAC （message authentication code） in multicast protocol is proposed in this paper, The digital watermarking which composes of the MAC of the significant vid eo content, the key and instant authentication data is embedded into the insignificant video component by the MLUT （modified look-up table） video watermarking technology. We explain a method that does not require storage of each data packet for a time, thus making receiver not vulnerable to DOS （denial of service） attack. So the video packets can be authenticated instantly without large volume buffer in the receivers. TESLA （timed efficient stream loss tolerant authentication） does not explain how to select the suitable value for d, which is an important parameter in multicast source authentication. So we give a method to calculate the key disclosure delay （number of intervals）. Simulation results show that the proposed algorithms improve the performance of data source authentication in multicast.展开更多
With the growth of digital media data manipulation in today’s era due to the availability of readily handy tampering software,the authenticity of records is at high risk,especially in video.There is a dire need to de...With the growth of digital media data manipulation in today’s era due to the availability of readily handy tampering software,the authenticity of records is at high risk,especially in video.There is a dire need to detect such problem and do the necessary actions.In this work,we propose an approach to detect the interframe video forgery utilizing the deep features obtained from the parallel deep neural network model and thorough analytical computations.The proposed approach only uses the deep features extracted from the CNN model and then applies the conventional mathematical approach to these features to find the forgery in the video.This work calculates the correlation coefficient from the deep features of the adjacent frames rather than calculating directly from the frames.We divide the procedure of forgery detection into two phases–video forgery detection and video forgery classification.In video forgery detection,this approach detect input video is original or tampered.If the video is not original,then the video is checked in the next phase,which is video forgery classification.In the video forgery classification,method review the forged video for insertion forgery,deletion forgery,and also again check for originality.The proposed work is generalized and it is tested on two different datasets.The experimental results of our proposed model show that our approach can detect the forgery with the accuracy of 91%on VIFFD dataset,90%in TDTV dataset and classify the type of forgery–insertion and deletion with the accuracy of 82%on VIFFD dataset,86%on TDTV dataset.This work can helps in the analysis of original and tempered video in various domain.展开更多
基金Supported bythe National Natural Science Foundationof China (60175001)
文摘A novel video data authentication model based on digital video watermarking and MAC （message authentication code） in multicast protocol is proposed in this paper, The digital watermarking which composes of the MAC of the significant vid eo content, the key and instant authentication data is embedded into the insignificant video component by the MLUT （modified look-up table） video watermarking technology. We explain a method that does not require storage of each data packet for a time, thus making receiver not vulnerable to DOS （denial of service） attack. So the video packets can be authenticated instantly without large volume buffer in the receivers. TESLA （timed efficient stream loss tolerant authentication） does not explain how to select the suitable value for d, which is an important parameter in multicast source authentication. So we give a method to calculate the key disclosure delay （number of intervals）. Simulation results show that the proposed algorithms improve the performance of data source authentication in multicast.
文摘With the growth of digital media data manipulation in today’s era due to the availability of readily handy tampering software,the authenticity of records is at high risk,especially in video.There is a dire need to detect such problem and do the necessary actions.In this work,we propose an approach to detect the interframe video forgery utilizing the deep features obtained from the parallel deep neural network model and thorough analytical computations.The proposed approach only uses the deep features extracted from the CNN model and then applies the conventional mathematical approach to these features to find the forgery in the video.This work calculates the correlation coefficient from the deep features of the adjacent frames rather than calculating directly from the frames.We divide the procedure of forgery detection into two phases–video forgery detection and video forgery classification.In video forgery detection,this approach detect input video is original or tampered.If the video is not original,then the video is checked in the next phase,which is video forgery classification.In the video forgery classification,method review the forged video for insertion forgery,deletion forgery,and also again check for originality.The proposed work is generalized and it is tested on two different datasets.The experimental results of our proposed model show that our approach can detect the forgery with the accuracy of 91%on VIFFD dataset,90%in TDTV dataset and classify the type of forgery–insertion and deletion with the accuracy of 82%on VIFFD dataset,86%on TDTV dataset.This work can helps in the analysis of original and tempered video in various domain.
