Deepfake Video Authentication Based on Blockchain

Author(s):  
Ujwal Patil ◽  
P.M. Chouragade
Keyword(s):  
Optik ◽  
2013 ◽  
Vol 124 (19) ◽  
pp. 3827-3834 ◽  
Author(s):  
Yanjiao Shi ◽  
Miao Qi ◽  
Yugen Yi ◽  
Ming Zhang ◽  
Jun Kong

2011 ◽  
Vol 145 ◽  
pp. 552-556 ◽  
Author(s):  
Grace C.W. Ting ◽  
Bok Min Goi ◽  
S. W. Lee

H.264/AVC is a widespread standard for high definition video (HD) for example DVD and HD videos on the internet. To prevent unauthorized modifications, video authentication can be used. In this paper, we present a cryptanalysis of a H.264/AVC video authentication scheme proposed by Saadi et al. [1] at EUSIPCO 2009. Our result will prevent situations where newer schemes are developed from the scheme thus amplifying the flaw. The designers claimed that the scheme can detect modifications on watermarked video. However, we show that an attacker can modify the watermarked video and compute a valid watermark such that the recipient will retrieve a watermark from the modified watermarked video that will match what the recipient computes during video authentication check. Thus, the recipient will think the tampered video is authentic. The first main problem of the scheme is its use of hash functions for watermark generation. Since hash functions are public functions not depending on any secret, the attacker can modify the watermarked video and feed this through the hash function to compute a new watermark. The second problem is that it is possible for the attacker to perform watermark embedding thus producing a modified watermarked video. On receiving the modified video, the recipient recomputes the watermark and compares this with the watermark extracted from the video. They will match because the embedded watermark and recomputed watermark use the same hash function based watermark generation and the same input i.e. the modified video. Our cryptanalysis strategy applies to any watermarking based video authentication scheme where the watermark and embedding are not functions of secrets. As countermeasure, the functions should be designed so that only legitimate parties can perform them. We present two improved schemes that solve this problem based on private key signing functions and message authentication functions respectively.


2005 ◽  
pp. 173-206
Author(s):  
Ching-Yung Lin

Multimedia authentication distinguishes itself from other data integrity security issues because of its unique property of content integrity in several different levels — from signal syntax levels to semantic levels. In this section, we describe several image authentication issues, including the mathematical forms of optimal multimedia authentication systems, a description of robust digital signature, the theoretical bound of information hiding capacity of images, an introduction of the self-authentication-and-recovery image (SARI) system, and a novel technique for image/video authentication in the semantic level. This chapter provides an overview of these image authentication issues.


Author(s):  
Saurabh Upadhyay ◽  
Sanjay Kumar Singh
Keyword(s):  

2019 ◽  
Vol 63 (7) ◽  
pp. 1017-1030 ◽  
Author(s):  
Zhenjun Tang ◽  
Lv Chen ◽  
Heng Yao ◽  
Xianquan Zhang ◽  
Chunqiang Yu

Abstract Video hashing is a novel technique of multimedia processing and finds applications in video retrieval, video copy detection, anti-piracy search and video authentication. In this paper, we propose a robust video hashing based on discrete cosine transform (DCT) and non-negative matrix decomposition (NMF). The proposed video hashing extracts secure features from a normalized video via random partition and dominant DCT coefficients, and exploits NMF to learn a compact representation from the secure features. Experiments with 2050 videos are carried out to validate efficiency of the proposed video hashing. The results show that the proposed video hashing is robust to many digital operations and reaches good discrimination. Receiver operating characteristic (ROC) curve comparisons illustrate that the proposed video hashing outperforms some state-of-the-art algorithms in classification between robustness and discrimination.


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