Robust and scalable video authentication issues and solutions 1

ACKNOWLEDGEMENT I want to say "Thank you" to the following people who have helped and guided me in my pursuit for my PHD degree. My first thanks go to my thesis supervisors: Dr Sun Qibin, Dr Ma Ruihua and Professor Huang Zhiyong. They have given me invaluable and specific guidance on my research activities, without which my path in research would be much more twisted. I cannot thank them enough for their contribution to make all my achievements a reality. My thanks also go to Professor Tian Qi, who has always been so caring and helpful to me. Every inspiring discussion I had with him gave me valuable rewards. I am not listing all the individuals who has helped me in one way or another, among them are my colleagues in Pervasive Media Laboratory: Zhang Zhishou, Ye Shuiming and Zhou Zhichen. They together have created a harmonious office atmosphere, which is extremely inductive to my research activities. I also want to extend a special thank to Wang Han of Cambridge University. He has helped me smooth the language used in every paper I wrote. No words can express my gratitude to my wife, Chen Yunping, who has - and will continue to – provide invaluable and indispensable support for my pursuit of dreams. She is the unsounded hero behind all my accomplishments. My lovely babies, He Jia and He Xu, also deserve my mention of appreciation for the happiness and colours they have brought to my life. And, finally, I would like to give my most sincere thanks to my parents, who have passed away a few years ago. Their love would forever live in my heart, giving me strength in my path to pursue the dreams of my life. i TABLE OF CONTENT ACKNOWLEDGEMENT .i TABLE OF CONTENT ii SUMMARY .vi LIST OF TABLES viii LIST OF FIGURES .ix CHAPTER 1.1 INTRODUCTION Robust Video Authentication 1.1.1 Objective .1 1.1.2 Source .3 1.1.3 Requirements 1.1.4 Models of video authentication .4 1.1.5 Object-based video authentication 1.1.6 Scalable video authentication .6 1.2 Theoretical Analysis .7 1.3 Structure of Thesis CHAPTER STATE-OF-THE-ART .10 2.1 Features in Image/Video Authentication 10 2.2 Image Authentication 13 2.2.1 Signature-based authentication .14 2.2.2 Watermarking-based authentication .19 2.2.3 Signature and watermarking based authentication .25 ii 2.3 Video Authentication 29 2.3.1 Frame-based video authentication 30 2.3.2 Object-based video authentication 33 CHAPTER A ROBUST OBJECT-BASED VIDEO AUTHENTICATION SYSTEM 36 3.1 Introduction .36 3.2 Introduction to MPEG4 Video Coding .39 3.3 Overview of the Proposed System 42 3.3.1 Targeted acceptable video processes 43 3.3.2 Brief system description .45 3.4 Feature Selection, Authentication Information Generation and Authenticity Verification .47 3.4.1 Feature selection .47 3.4.2 Authentication information generation and authenticity verification .56 3.5 Object-based Video Watermarking Algorithm (1) .60 3.5.1 Challenges and solutions for object-based watermark embedding and extraction .60 3.5.2 Watermark embedding 63 3.5.3 Watermark extraction .65 3.5.4 Evaluation of the watermarking algorithm .66 3.6 Object-based Video Watermarking Algorithm (2) .69 3.6.1 Two important techniques 71 3.6.2 Watermark embedding 74 3.6.3 Watermark extraction .75 3.6.4 Evaluation of the watermarking algorithm .76 iii 3.7 Experimental Results 78 3.8 Summary and Future Works .82 CHAPTER A SCALABLE VIDEO AUTHENTICATION SYSTEM .84 4.1 Introduction .84 4.2 Brief Introduction to Video Streaming and Transcoding .89 4.3 Overview of the Proposed Scheme .92 4.3.1 System general requirements and overview .92 4.3.2 Countermeasures to the transcoding .95 4.4 Authentication Information Generation and Authenticity Verification 98 4.4.1 Authentication information generation .98 4.4.2 Authenticity verification .100 4.5 Video Watermarking Scheme Robust to Transcoding .101 4.5.1 Watermark embedding 103 4.5.2 Watermark extraction .108 4.6 Experimental Results and System Performance Analysis 109 4.7 Summary and Future Works .113 CHAPTER THEORETICAL ANALYSYS ON SELF-EMBEDDING VIDEO AUTHENTICATION SYSTEM 114 5.1 Introduction .114 5.2 Relation between Feature Difference and Video Distortion .119 5.2.1 Mutual information and rate distortion function .119 5.2.2 Feature difference vs. video distortion 120 5.2.3 Results of evaluation .131 5.2.4 Summary .134 iv 5.3 Watermarking Capacity 134 5.3.1 General watermarking capacity 135 5.3.2 Specific watermarking capacity 138 CHAPTER CONCLUSIONS AND FUTURE WORKS 147 6.1 Conclusions .147 6.2 Future Works 149 REFERENCE 150 APPENDIX 167 v SUMMARY As a by-product of the rapid development of digital technologies, attacks on valuable video without any noticeable degradation to the video quality become easy. Video authentication aims to ensure the trustworthiness of the video by verifying the integrity and the source of the video data. In this thesis, we investigate the issues in designing a video authentication system and then propose two solutions using technologies such as digital signature, watermarking, error correction coding (ECC), etc. Both are secure, robust and content authentication systems, in which the received video is considered as authentic as long as the video content remains unchanged. These two systems, however, have different design considerations because they are for different applications. The first proposed system is an object-based video authentication system for MPEG4-based video applications. This system can tolerate object-based processing such as object-segmentation and RST (rotation, scaling and translation) besides the normal MPEG compression; on the other hand, object-based attacks, such as object modifications, object replacement and background replacement can be detected. To protect the integrity of video, we first propose to generate authentication information using features of both the object and background. The second proposed system is a scalable video authentication system for video streaming. We mainly focus on video transcoding, including format conversion, frame dropping and requantization. While being robust to such transcoding, the system is capable of detecting malicious attacks such as content modification and injection of commercials or offensive materials into the video stream. Compressed-domain processing not only reduces the computation complexity of the proposed method but also makes the proposed method compliant with the state-of-the-art transcoders as most of them are performed in the DCT domain rather than the pixel domain. vi In addition, we present our works in analyzing the two important components, feature selection and watermarking capacity, in a “self-embedding” video authentication system, which covers the two proposed systems. In this analysis, we theoretically analyze the relationship between video distortion and its introduced feature difference once a certain set of feature is selected based on the requirements of completeness, sensitivity and robustness. Furthermore, we estimate the capacity of watermarking schemes, which are designed for authentication applications, from the point of reliable detection instead of information theory. vii LIST OF TABLES Table 2-1 Features for image/video authentication 13 Table 3-1 Acceptable video processing and their parameters for system evaluation .44 Table 3-2 bits Quantization 51 Table 3-3 bits Quantization 51 Table 3-4 System performance when the video object undergoes various video processes .79 Table 4-1 The relationships between DCT coefficients in the QCIF video and their corresponding DCT coefficients in the CIF video 107 Table 4-2 Bit-rate comparison before and after watermarking .112 viii LIST OF FIGURES Fig 1.1 General model for video authentication using digital signature and watermarking Fig 2.1 Public key Digital Signature Scheme (DSS) 14 Fig 2.2 Signature-based image authentication system 15 Fig 2.3 Block-diagram of integrity verification in [13] 16 Fig 2.4 A general model of authentication solutions that using digital signature and watermarking technologies .27 Fig 2.5 Image authentication classification 28 Fig 2.6 A pactical solution for stream signing 33 Fig 3.1 An example of video surveillance system. .39 Fig 3.2 Block diagram of the proposed system. .39 Fig 3.3 A video frame is a composition of one video object and background .40 Fig 3.4 Block-diagram of object-based video coding .41 Fig 3.5 Structure of VOP encoder 42 Fig 3.6 VOP Formation 42 Fig 3.7 Attack that only modify object content while preserving its shape 49 Fig 3.8 36 normalized ART coefficients from the first VOP of video “Akiyo” .49 Fig 3.9 Videos for feature selection evaluation. .52 Fig 3.10 The maximum Hamming distances between feature vectors of the original object and object having undergone normal processes .54 Fig 3.11 Mask difference between the original and processed objects. This difference is similar to the segmentation error. .55 Fig 3.12 The distance between feature vectors of different video objects .55 Fig 3.13 Procedure for authentication information generation 57 ix Fig 3.14 Illustration of authentication information generation 58 Fig 3.15 Procedure for authentication verification. .59 Fig 3.16 16 DFT coefficients grouping for watermarking. The 61 Fig 3.17 Comparison between DFT of the original and scaled images. 63 Fig 3.18 Area classification in the DFT domain 63 Fig 3.19 Surveillance video “Dajun” .66 Fig 3.20 PSNR of watermarked video “Akiyo” . .67 Fig 3.21 Bit Error Rate (BER) of the extracted watermark under video processes .68 Fig 3.22 Block diagram of watermark embedding 70 Fig 3.23 Block diagram of watermark extraction 70 Fig 3.24 Four adjacent sampling points around A ' .73 Fig 3.25 Watermarking area in the LPM domain 75 Fig 3.26 PSNR of watermarked video .76 Fig 3.27 The robustness to acceptable video processes .77 Fig 3.28 Robustness to compression 77 Fig 3.29 Comparison between the original and signed video objects (“Akiyo”). .79 Fig 3.30 The relationship between the correctly authenticated video frames and the scaling factor. 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Before discussion, we will give the notation used in this discussion. A.1 Notation a. In MPEG 1/2 coding, the DCT coefficient quantization consists of two steps: weight matrix ( W ) quantization and quantization scale ( Q ) quantization. For convenience, we use the quantization step, which can be calculated as round ( *W * Q ) , to combine these 16 two steps. In this analysis, Qa represents the maximum quantization step, which the system is supposed to be robust; Qc represents the quantization step for encoding the watermarked video; and Qc' represents the quantization step for video transcoding. Both Qc and Qc' are less than or equal to Qa . b. The matrix form of CIF to QCIF conversion, equation (4-1), is rewritten in terms of individual DCT coefficient as follows ∑α b i i b= i =1 (A-1) 167 where b , b1 , b2 , b3 and b4 refer to the DCT coefficients in blocks B, B1, B2, B3 and B4 as (defined in Fig 4.5) respectively; a1 , a , a and a refer to the weight for each block, which can be obtained from Q1 and Q2 in equations (4-2) and (4-3). c. If b , b1 , b2 , b3 and b4 represent the watermarked DCT coefficients, according to the watermarking scheme, we have ⎧b = mQa ⎪ ⎨ ⎪⎩bi = mi Qa i = 1,2,3,4 (A-2) where m , m1 , m , m3 and m are integer values. A.2 Intra Macro-block Before video transcoding, video stream is decoded into a series of DCT coefficients frames. If b1' , b2' , b3' , and b4' represent the reconstructed version of DCT coefficients b1 , b2 , b3 and b4 , b1' , b2' , b3' and b4' can be calculated as bi' = round ( bi )Qc Qc i = 1,2,3,4 = bi + β i * Qc (A-3) (A-4) where − 0.5 ≤ β i < 0.5 . In our proposed system, video transcoding include re-quantization, CIF to QCIF conversion, or the combination of these two processes. In this appendix, we will only analyze the performance of the watermarking scheme in the combined process. Video transcoding 168 • Firstly, the CIF video frame is converted into the QCIF video frame. If b ' represents the DCT coefficient in the QCIF video frame, substituting bi' and b ' into equation (A-1), we ∑α b ' i i b' = have • i =1 (A-5) Secondly, DCT coefficients in the QCIF video frame are quantized by another quantizer step Qc' , which can be represented as round ( b' ). Qc' Watermark retrieval During watermark retrieving, the DCT coefficient in the QCIF video frame ( b '' ) can be reconstructed as b '' = round ( b' )Qc' ' Qc (A-6) = b ' + β ' * Qc' − 0.5 ≤ β ' < 0.5 (A-7) Now, we will show that b ' in equation (A-7) can be replaced by b if a post-process, called “Error Compensation”, is employed after watermark embedding. Substituting equation (A-4) into equation (A-5), equation (A-5) can be re-written as b' = ∑ α i bi + ∑ β i Qc i =1 i =1 (A-8) 4 ∑a β Q i =b+ i =1 i c (A-9) 169 ∑a β Q i Obviously, i c i =1 can be calculated according to equation (A-4) before the watermarked video is compressed because Qc , b1 , b2 , b3 and b4 are all known in the encoder. So, it can be reduced by modifying the watermarked DCT coefficients b1 , b2 , b3 and b4 slightly. We have explained this in difference mapping algorithm of watermark embedding scheme in Section 4.5.1. During this modification process, ∑α b i i must remain unchanged; and the video quality must not i =1 be degraded significantly. We define this process as “Error Compensation” process. As we were developing the proposed video authentication system, we found that “Error Compensation” process is feasible and useful. So, equation (A-7) can be simplified to b '' = b + β ' * Qc' (A-10) Thus, round ( β ' * Qc' b '' ) = m + round ( ) Qa Qa (A-11) Since QC' < Qa , equation (A-11) can be re-written as round ( b '' b ) = m = round ( ) Qa Qa (A-11) Therefore, the embedded watermark can be correctly detected. A.3. Inter Macro-block Analyzing the performance of the watermarking scheme in inter macro-block is more complicated than that in intra macro-block due to the existence of “dead zone” during quantization. The quantization can be represented as 170 ⎧ bi − bip ) ⎪⎪ floor ( Qc ni = ⎨ ⎪0 ⎪⎩ if | bi − bip |≥ Qc i = 1,2,3,4 (A-12) else where bip is the predicted value of bi . If we also use b1' , b2' , b3' , and b4' to represent the reconstructed version of DCT coefficients b1 , b2 , b3 and b4 , b1' , b2' , b3' and b4' can be calculated as bi' ⎧bi + β i * Qc ⎪ =⎨ ⎪bi + γ i * Qc ⎩ if ni ≠ (A-13) else where − 0.5 < β i ≤ 0.5 , and − < γ i ≤ . Video transcoding For the case of inter macro-block, we will also only analyze the performance of the watermarking scheme in the combined process. • Firstly, the CIF video frame is converted to the QCIF video frame. If b ' represents the DCT coefficient in the QCIF video frame, it can be calculated as equation (A-5). Again, by employing “Error Compensation” process after watermark embedding, we can have b ' ≈ b for both ni ≠ and ni = . • Secondly, DCT coefficients in the QCIF video frame are quantized by another quantizer step Qc' , which can be represented as ⎧ b ' − (b p ) ' floor ( ) if | b ' − (b p ) ' |≥ QC' ⎪⎪ Q c k =⎨ ⎪0 else ⎪⎩ (A-14) where (b p ) ' is the predicted value of b ' . Watermark retrieval 171 The DCT coefficient of the QCIF video frame can be reconstructed as ⎧b ' + β ' * Qc' ⎪ b =⎨ ' ' ' ⎪b + γ * Qc ⎩ k ≠0 if '' (A-15) else where − 0.5 < β ' ≤ 0.5 , and − < γ ' ≤ . Since b ' ≈ b , equation (A-15) can be re-written as ⎧b + β ' * Qc' ⎪ b '' = ⎨ ' ' ⎪b + γ * Qc ⎩ if k ≠0 (A-16) else Thus, ⎧ b + β ' * Qc' ( ) round ⎪ Qa b '' ⎪ round ( ) = ⎨ ' ' Qa ⎪round ( b + γ * Qc ) ⎪ Qa ⎩ ⎧ β ' * Qc' ) ⎪m + round ( Qa ⎪ =⎨ ' ' ⎪m + round ( γ * Qc ) ⎪ Qa ⎩ Since QC' < Qa , round ( equation round ( β ' * Qc' Qa k ≠0 if (A-17) else if k≠0 (A-18) else ) is equal to zero. Therefore, in the case that k ≠ , such an b '' b ) = round ( ) exists. That is, the embedded watermark can be correctly Qa Qa detected in this case. Now, we evaluate the value of item round ( γ ' * Qc' Qa assumed to be zero, the probability that round ( ) . Although round ( γ ' * Qc' Qa γ ' * Qc' Qa ) cannot be simply ) is equal to zero is large from the following analysis. 172 Since the quantization step is calculated by round ( *W * Q ) , for the same quantization scale 16 ( Q ), the quantization step in intra macro-block is different from that in inter macro-block because the weighting matrix of intra macro-block is different from that of inter macro-block. In our proposed video authentication system, if Qa represents the maximum quantization step for a robust system, the maximum value of Qc' must be Qa in the case of intra macro-block. But, for inter macro-block, the maximum value of Qc' is not Qa , but 16 Qa . So, γ ' is uniformly distributed in 26 the range of [-16/26, 16/26]. Therefore, the probability that round ( γ ' * Qc' Qa ) is zero, or the probability that the embedded watermark can be correctly detected, is about 80% in this case even if all macro-blocks in one video frame are quantized using maximum quantization step ( Qa ). If the probability of k ≠ in equation (A-18) is p ( ≤ p ≤ ), the probability that watermark can be retrieved is p + 0.8(1 − p) = 0.8 + 0.2 p in the case of inter macro-block, which is high. 173 [...]... 11 0 Fig 4 .13 Bit Error Rate of the extracted watermark under transcoding 11 0 Fig 4 .14 Bit Error Rate of the extracted watermark under transcoding: re-quantization and CIF to QCIF conversion 11 0 Fig 4 .15 PSNR of video before and after signing 11 2 Fig 4 .16 The signed video vs its attacked video 11 3 Fig 5 .1 A “self-embedding” authentication system 11 5 Fig 5.2... Sun, Dajun He and Qi Tian, “A Secure and Robust Authentication Scheme for Video Transcoding”, submitted to IEEE CSVT xii CHAPTER 1 INTRODUCTION This thesis addresses the problem of video authentication After examining the issues of video authentication, some solutions for these issues will be provided 1. 1 Robust Video Authentication With the rapid development in digital technologies, video applications... Block-diagram of the scalable video authentication system 93 Fig 4.7 Diagram of the proposed solution that is robust to video transcoding .94 Fig 4.8 Illustration of authentication information generation .10 0 Fig 4.9 Procedure of authenticity verification 10 1 Fig 4 .10 Watermark extraction .10 2 Fig 4 .11 First frames of 5 testing videos employed for evaluation 10 9 Fig 4 .12 Bit Error... .11 7 Fig 5.3 Relationships among visual quality, robustness and amount of embedded information 11 8 Fig 5.4 Feature difference and video distortion 12 2 Fig 5.5 Relationship among H (V0 ) , H (V0 V2 ) and I (V0 ; V2 ) .12 6 Fig 5.6 Maximum tolerable feature differences for video “Akiyo” 13 2 Fig 5.7 Relationship between feature difference and quantization step .13 3 Fig... system’s robustness to normal video processes is guaranteed by ECC and watermarking; and the system’s security is protected by cryptographic hash [3] 1. 1.6 Scalable video authentication With the rapid progresses in multimedia and broadband network technologies, advanced multimedia services become more and more popular Examples of such services include videoconference, distance learning, networked video, and. .. in the area of video authentication Works in image authentication will also be reviewed since they induce many video authentication solutions In fact, some image authentication solutions can be directly employed in the video authentication if a video sequence is considered as a series of video frames In Chapter 3, we will propose an object-based video authentication solution, which is robust to incidental... watermarking, authentication solutions could be divided into lossless authentication and lossy authentication; According to their robustness to acceptable manipulations, authentication solutions could be divided into fragile authentication and semi-fragile authentication Furthermore, some 19 authentication solutions employed multiple watermarks for different purposes Although lossless authentication. .. coefficients [33,35] Image Authentication As stated in Chapter1, authentication solutions can be classified into 3 categories: signature-based authentication, watermarking-based authentication, and signature and watermarking based 13 authentication Thus, we will review previous works in this order and finally summarize these works in Fig 2.5 2.2 .1 Signature-based authentication Data authentication solution... caused by the use of a symmetric key in watermark embedding and extraction [8] Thus, latest authentication solutions tend to combine signature-based authentication and watermarking-based authentication together A general model of such video authentication solutions is shown in Fig 1. 1 In the providing site (or sending site), features of the video, together with the user defined information, is encrypted... authentication, we will propose two video authentication solutions for different video applications in this thesis 1. 1.5 Object-based video authentication Nowadays, the object-based MPEG4 standard [9] is becoming growingly attractive to various applications in areas such as the Internet, video editing and wireless communication because of its 5 object-based nature For instance, in video editing, it is the object . CHAPTER 1 INTRODUCTION 1 1. 1 Robust Video Authentication 1 1. 1 .1 Objective 1 1. 1.2 Source 3 1. 1.3 Requirements 3 1. 1.4 Models of video authentication 4 1. 1.5 Object-based video authentication. conversion 11 0 Fig 4 .15 PSNR of video before and after signing 11 2 Fig 4 .16 The signed video .vs. its attacked video 11 3 Fig 5 .1 A “self-embedding” authentication system 11 5 Fig 5.2. authentication 5 1. 1.6 Scalable video authentication 6 1. 2 Theoretical Analysis 7 1. 3 Structure of Thesis 8 CHAPTER 2 STATE-OF-THE-ART 10 2 .1 Features in Image /Video Authentication 10 2.2 Image Authentication