An Anti-Forensics Video Forgery Detection Method Based on Noise Transfer Matrix Analysis
Abstract
:1. Introduction
- Multiple recoding traces are solid proof of a loss of originality. However, multiple recoding traces may not indicate that the video has necessarily been tampered with for reasonable transmission processes.
- Integral GOP frame deletion in static scenes is an effective attack against most multiple recoding detection approaches based on Wang et al.’s [35] principle.
- We propose a novel anti-forensic detection approach to discern forged videos (integral GOP frame deletion in static scenes), using the combination of a pyramid structure and an adaptive weight adjustment module.
- We adopt a pooling operation and pyramid structure to extract noise features, which are available for subsequent analysis with suppressed sensitivity.
- A normalization operation and the combination of successive frame results reduce the influence of variable dimensions and video recording interference.
- Incorporating an adaptive weight adjustment module ensures the algorithm’s universality and fast learning ability with only a single video and diverse environments, thus meeting the practical requirements of forensic science.
- Original videos and visual examples are discussed in the paper, which are intuitive and detailed and display the characteristics of various detection methods.
- The receiver operating characteristic (ROC) results demonstrate significant enhancement, particularly in the low false positive rate (FPR), indicating highly improved performance in terms of the forensic principle of no punishment in doubtful cases.
2. Related Works
2.1. Traditional Cue-Based Detection Methods
2.2. Equipment Trace Detection Methods
2.3. Network-Based Detection Methods
2.4. Double Encoding Detection Methods
3. Proposed Approach
3.1. Methods
3.1.1. Noise Extraction
3.1.2. Noise Transfer Matrix Computation
3.1.3. Adjusting Transfer Matrix Weights
4. Experiments
4.1. Dataset
4.2. Coarse Filter
4.3. Parameter Optimization Experiments
4.4. Ablation Experiments
4.5. Comparison Experiments
4.5.1. LBP Method
4.5.2. MNMI Method
4.5.3. Haralick Coded Method
4.5.4. MS-SSIM Method
4.5.5. UFS-MSRC Method
4.5.6. Learning-Based Method Results
4.5.7. Results of Robustness to Anti-Forensics Operation
4.6. Discussion
- (1)
- The LBP, Haralick and MNMI algorithms detect suspected frame deletion points more accurately than random. Even though the ROC curves demonstrate poor performance, there is no doubt that those methods still provide useful information. False positive detection chance represents the dominant quantity in the video. Even considering the periodic effect caused by the GOP structure, the number of possible false positive detection points is dozens of times that of genuine positive detection points. An intuitive example is shown in Figure 12. Points indicated by blue circles indicate participants of the genuine FDP points.
- (2)
- Our approach has improved performance in detecting delicate frame deletion (integral GOP deletion in a static scene) in video compared with other approaches [1,33,34]. From Table 5, we can identify multiple examples of genuine FDPs detected by our approach, while these are ignored by the other approaches. Our approach reaches a TPR of 0.4 with an FPR of 0. However, the compared methods do not reach higher than 0.05. Because of the rigorousness of forensic science, vague clues cannot be accepted as evidence. A high TPR without any dispute can make a vital contribution to court proceedings.
- (3)
- Our approach reduces the possibility of false positive detection caused by various types of interference in video generation. This is a key point in forensic science in terms of verifying the authenticity of a video. In practical work, many novel approaches have been discarded due to the problem of false positive detection. In court, a suspected false positive detection in an entire video could overturn the results of genuine FDP detection. Examples will be given, presenting the characteristics of the proposed approach and comparing the approaches directly.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Scene | 16 × 9 True Positive | 16 × 9 False Positive | 32 × 18 True Positive | 32 × 18 False Positive | 64 × 36 True Positive | 64 × 36 False Positive |
---|---|---|---|---|---|---|
1 | / | 1.5 | / | 1.5 | / | 1.5 |
2 | / | 2.5 | / | 2 | / | 2 |
3 | / | 2.5 | / | 2.5 | / | 2.5 |
4 | / | 3 | / | 3 | / | 2.5 |
5 | / | 2 | / | 2.5 | / | 2 |
6 | / | 2 | / | 2 | / | 2 |
7 | / | 2 | / | 2 | / | 2 |
8 | / | 2 | / | 2.5 | / | 2.5 |
9 | / | 2 | / | 2.5 | / | 2 |
10 | / | 3 | / | 2.5 | / | 2.5 |
11 | / | 2.5 | / | 2 | / | 2 |
12 | / | 3 | / | 3 | / | 2.5 |
13 | / | 2 | / | 2 | / | 2 |
14 | / | 2 | / | 2.5 | / | 2.5 |
15 | / | 2 | / | 2 | / | 2 |
16 | / | 2.5 | / | 2 | / | 2 |
17 | / | 2.5 | / | 2 | / | 2 |
18 | / | 2 | / | 2 | / | 2 |
19 | / | 2.5 | / | 2.5 | / | 2.5 |
20 | / | 3 | / | 2.5 | / | 2.5 |
21 | 4 | 2.5 | 3 | 2.5 | 2.5 | 2 |
22 | 3 | 3 | 3 | 3 | 3 | 3 |
23 | 4 | 3.5 | 3 | 2.5 | 2.5 | 2.5 |
24 | 3.5 | 3.5 | 3 | 2.5 | 2.5 | 2.5 |
25 | 4.5 | 3.5 | 3 | 3 | 3 | 3 |
26 | 3.5 | 2.5 | 2.5 | 2 | 2.5 | 2 |
27 | 5 | 3.5 | 3.5 | 3 | 3 | 2.5 |
28 | 3.5 | 3 | 3.5 | 3 | 3 | 3 |
29 | 4 | 4.5 | 3.5 | 3.5 | 3 | 3 |
30 | 4 | 4.5 | 3.5 | 3.5 | 3 | 3 |
31 | 6 | 4 | 4.5 | 3 | 3 | 3 |
32 | 5 | 3.5 | 4 | 3.5 | 3.5 | 3 |
33 | 5.5 | 3 | 4 | 3 | 3 | 3 |
34 | 5.5 | 3.5 | 4 | 3.5 | 3 | 3 |
35 | 4.5 | 4.5 | 4 | 4 | 3.5 | 3.5 |
36 | 3.5 | 3.5 | 3 | 3 | 2.5 | 2.5 |
37 | 4.5 | 4.5 | 4 | 4 | 3 | 3 |
38 | 4.5 | 3.5 | 3.5 | 3 | 3 | 3 |
39 | 4 | 3 | 3 | 3 | 3 | 3 |
40 | 3.5 | 5 | 3.5 | 3.5 | 3 | 3 |
41 | / | 2 | / | 2 | / | 2 |
42 | / | 2 | / | 2 | / | 2 |
43 | / | 2.5 | / | 2.5 | / | 2.5 |
44 | / | 2.5 | / | 2.5 | / | 2 |
45 | / | 2.5 | / | 2.5 | / | 2 |
46 | / | 2.5 | / | 2.5 | / | 2 |
47 | / | 3 | / | 2.5 | / | 2.5 |
48 | / | 3 | / | 2.5 | / | 2.5 |
49 | / | 2.5 | / | 2.5 | / | 2 |
50 | / | 2.5 | / | 2.5 | / | 2 |
51 | / | 3 | / | 2 | / | 2 |
52 | / | 3 | / | 3 | / | 2.5 |
53 | / | 4 | / | 2.5 | / | 2.5 |
54 | / | 3.5 | / | 3 | / | 2.5 |
55 | / | 2.5 | / | 2.5 | / | 2.5 |
56 | / | 3 | / | 3 | / | 3 |
57 | / | 4.5 | / | 3.5 | / | 3 |
58 | / | 2.5 | / | 2 | / | 2 |
59 | / | 3 | / | 2.5 | / | 2 |
60 | / | 2.5 | / | 2.5 | / | 2.5 |
61 | 5.5 | 4 | 4 | 4 | 3.5 | 3 |
62 | 5 | 4.5 | 3.5 | 3.5 | 3 | 3 |
63 | 5 | 5 | 3.5 | 3.5 | 3 | 3 |
64 | 5.5 | 4.5 | 5 | 4 | 3.5 | 3.5 |
65 | 6 | 4 | 4 | 3.5 | 4 | 3 |
66 | 4.5 | 3 | 3.5 | 3 | 3 | 3 |
67 | 5.5 | 4 | 4 | 3.5 | 3.5 | 3.5 |
68 | 5 | 3 | 3.5 | 3 | 3 | 2.5 |
69 | 4 | 3 | 3 | 3 | 3 | 3 |
70 | 4.5 | 3 | 3.5 | 3.5 | 3.5 | 3.5 |
71 | 3.5 | 3.5 | 3 | 3 | 2.5 | 2.5 |
72 | 6 | 4.5 | 4.5 | 4 | 3.5 | 3.5 |
73 | 6 | 4 | 4.5 | 3.5 | 3.5 | 3 |
74 | 4.5 | 4.5 | 4 | 4 | 3.5 | 3.5 |
75 | 3.5 | 3 | 3.5 | 3 | 3 | 3 |
76 | 3.5 | 3 | 3 | 3 | 2.5 | 2.5 |
77 | 2.5 | 4 | 2.5 | 3.5 | 2.5 | 3 |
78 | 6 | 3.5 | 5 | 3.5 | 4 | 3 |
79 | 5.5 | 4 | 4 | 3.5 | 3.5 | 3 |
80 | 5 | 4 | 4.5 | 3.5 | 3 | 3 |
Scene | 0.75 True Positive | 0.75 False Positive | 1 True Positive | 1 False Positive | 1.25 True Positive | 1.25 False Positive |
---|---|---|---|---|---|---|
1 | / | 2 | / | 1.5 | / | 2.5 |
2 | / | 2.5 | / | 2.5 | / | 2.5 |
3 | / | 2.5 | / | 2.5 | / | 3 |
4 | / | 2.5 | / | 3 | / | 3 |
5 | / | 2.5 | / | 2 | / | 2.5 |
6 | / | 2 | / | 2 | / | 2.5 |
7 | / | 2 | / | 2 | / | 3 |
8 | / | 2.5 | / | 2 | / | 2.5 |
9 | / | 2.5 | / | 2 | / | 3 |
10 | / | 2.5 | / | 3 | / | 2.5 |
11 | / | 3.5 | / | 2.5 | / | 3 |
12 | / | 3 | / | 3 | / | 3 |
13 | / | 2.5 | / | 2 | / | 3 |
14 | / | 2.5 | / | 2 | / | 3 |
15 | / | 2 | / | 2 | / | 2.5 |
16 | / | 2.5 | / | 2.5 | / | 2.5 |
17 | / | 2.5 | / | 2.5 | / | 3 |
18 | / | 2 | / | 2 | / | 3 |
19 | / | 2.5 | / | 2.5 | / | 2.5 |
20 | / | 2.5 | / | 3 | / | 2.5 |
21 | 3.5 | 2.5 | 4 | 2.5 | 3.5 | 2.5 |
22 | 3 | 2.5 | 3 | 3 | 3.5 | 2.5 |
23 | 3.5 | 3.5 | 4 | 3.5 | 3 | 3 |
24 | 3.5 | 3.5 | 3.5 | 3.5 | 4 | 3.5 |
25 | 3.5 | 3 | 4.5 | 3.5 | 3 | 3 |
26 | 3.5 | 3 | 3.5 | 2.5 | 3 | 3.5 |
27 | 4.5 | 3.5 | 5 | 3.5 | 3.5 | 4 |
28 | 3.5 | 3 | 3.5 | 3 | 3.5 | 3 |
29 | 4 | 4 | 4 | 4.5 | 3 | 3 |
30 | 4 | 4 | 4 | 4.5 | 3.5 | 4.5 |
31 | 5 | 4 | 6 | 4 | 4.5 | 4 |
32 | 5 | 4 | 5 | 3.5 | 4.5 | 4 |
33 | 4 | 2.5 | 5.5 | 3 | 3 | 3.5 |
34 | 5 | 3 | 5.5 | 3.5 | 3.5 | 3 |
35 | 4 | 4 | 4.5 | 4.5 | 4.5 | 4 |
36 | 3.5 | 3.5 | 3.5 | 3.5 | 3 | 3 |
37 | 4.5 | 4 | 4.5 | 4.5 | 3.5 | 4 |
38 | 4.5 | 3.5 | 4.5 | 3.5 | 4.5 | 3.5 |
39 | 3 | 2.5 | 4 | 3 | 3.5 | 3 |
40 | 3.5 | 5 | 3.5 | 5 | 4 | 5 |
41 | / | 2.5 | / | 2 | / | 3 |
42 | / | 3 | / | 2 | / | 3 |
43 | / | 2.5 | / | 2.5 | / | 2.5 |
44 | / | 2.5 | / | 2.5 | / | 2.5 |
45 | / | 3 | / | 2.5 | / | 3 |
46 | / | 3 | / | 2.5 | / | 3.5 |
47 | / | 3 | / | 3 | / | 3.5 |
48 | / | 2.5 | / | 3 | / | 2.5 |
49 | / | 2.5 | / | 2.5 | / | 3 |
50 | / | 3.5 | / | 2.5 | / | 3 |
51 | / | 3 | / | 3 | / | 3 |
52 | / | 3.5 | / | 3 | / | 3.5 |
53 | / | 4 | / | 4 | / | 3.5 |
54 | / | 3.5 | / | 3.5 | / | 3.5 |
55 | / | 2.5 | / | 2.5 | / | 2.5 |
56 | / | 3 | / | 3 | / | 3.5 |
57 | / | 4 | / | 4.5 | / | 4.5 |
58 | / | 2.5 | / | 2.5 | / | 2 |
59 | / | 3 | / | 3 | / | 3 |
60 | / | 2.5 | / | 2.5 | / | 3 |
61 | 4.5 | 4 | 5.5 | 4 | 4.5 | 4 |
62 | 4.5 | 4 | 5 | 4.5 | 4.5 | 4 |
63 | 5 | 5 | 5 | 5 | 4.5 | 4.5 |
64 | 5 | 4.5 | 5.5 | 4.5 | 5 | 4.5 |
65 | 5.5 | 4.5 | 6 | 4 | 6 | 5 |
66 | 4.5 | 3 | 4.5 | 3 | 4.5 | 3 |
67 | 4.5 | 3.5 | 5.5 | 4 | 3.5 | 3 |
68 | 4 | 3 | 5 | 3 | 3.5 | 3 |
69 | 3.5 | 3 | 4 | 3 | 4 | 3.5 |
70 | 4 | 3 | 4.5 | 3 | 3.5 | 3.5 |
71 | 3.5 | 3.5 | 3.5 | 3.5 | 4 | 4 |
72 | 5.5 | 4.5 | 6 | 4.5 | 4 | 4 |
73 | 5 | 4 | 6 | 4 | 4.5 | 4 |
74 | 4.5 | 4 | 4.5 | 4.5 | 4 | 3.5 |
75 | 3.5 | 3.5 | 3.5 | 3 | 4 | 4.5 |
76 | 3 | 3 | 3.5 | 3 | 3.5 | 3 |
77 | 2.5 | 4 | 2.5 | 4 | 3 | 4 |
78 | 5 | 3.5 | 6 | 3.5 | 3.5 | 3.5 |
79 | 4.5 | 3.5 | 5.5 | 4 | 4 | 4 |
80 | 4 | 4 | 5 | 4 | 4.5 | 4 |
Scene | = 2 True Positive | = 2 False Positive | = 3 True Positive | = 3 False Positive | = 4 True Positive | = 4 False Positive |
---|---|---|---|---|---|---|
1 | / | 2 | / | 1.5 | / | 2.5 |
2 | / | 2.5 | / | 2.5 | / | 3 |
3 | / | 2.5 | / | 2.5 | / | 2.5 |
4 | / | 3 | / | 3 | / | 3 |
5 | / | 2.5 | / | 2 | / | 2.5 |
6 | / | 2 | / | 2 | / | 2.5 |
7 | / | 2 | / | 2 | / | 2.5 |
8 | / | 2.5 | / | 2 | / | 2.5 |
9 | / | 2 | / | 2 | / | 2.5 |
10 | / | 3 | / | 3 | / | 2.5 |
11 | / | 3 | / | 2.5 | / | 3.5 |
12 | / | 3 | / | 3 | / | 3 |
13 | / | 2.5 | / | 2 | / | 3 |
14 | / | 2 | / | 2 | / | 2.5 |
15 | / | 2 | / | 2 | / | 2 |
16 | / | 2.5 | / | 2.5 | / | 2.5 |
17 | / | 2.5 | / | 2.5 | / | 3 |
18 | / | 2.5 | / | 2 | / | 2.5 |
19 | / | 2.5 | / | 2.5 | / | 2.5 |
20 | / | 3 | / | 3 | / | 3 |
21 | 3.5 | 2.5 | 4 | 2.5 | 4 | 3.5 |
22 | 3 | 3 | 3 | 3 | 3.5 | 3.5 |
23 | 3.5 | 3 | 4 | 3.5 | 3 | 3.5 |
24 | 3.5 | 3.5 | 3.5 | 3.5 | 4 | 3.5 |
25 | 3.5 | 3.5 | 4.5 | 3.5 | 3.5 | 3 |
26 | 3 | 2.5 | 3.5 | 2.5 | 3 | 3 |
27 | 4 | 3.5 | 5 | 3.5 | 4.5 | 4 |
28 | 3.5 | 3 | 3.5 | 3 | 3.5 | 4 |
29 | 4 | 3 | 4 | 4.5 | 3.5 | 5 |
30 | 4 | 4 | 4 | 4.5 | 4 | 4.5 |
31 | 4.5 | 3.5 | 6 | 4 | 5 | 4.5 |
32 | 4.5 | 3.5 | 5 | 3.5 | 4.5 | 4 |
33 | 4.5 | 3 | 5.5 | 3 | 5.5 | 4 |
34 | 5 | 3 | 5.5 | 3.5 | 4.5 | 4.5 |
35 | 4 | 4 | 4.5 | 4.5 | 4.5 | 4 |
36 | 3 | 3 | 3.5 | 3.5 | 4.5 | 4.5 |
37 | 4.5 | 4 | 4.5 | 4.5 | 4 | 4 |
38 | 4 | 3.5 | 4.5 | 3.5 | 5 | 4 |
39 | 3.5 | 3 | 4 | 3 | 3.5 | 3 |
40 | 3.5 | 5 | 3.5 | 5 | 4.5 | 5.5 |
41 | / | 2.5 | / | 2 | / | 3 |
42 | / | 2.5 | / | 2 | / | 3 |
43 | / | 2.5 | / | 2.5 | / | 3 |
44 | / | 2.5 | / | 2.5 | / | 3 |
45 | / | 2.5 | / | 2.5 | / | 3 |
46 | / | 2.5 | / | 2.5 | / | 3 |
47 | / | 3 | / | 3 | / | 3.5 |
48 | / | 2.5 | / | 3 | / | 2.5 |
49 | / | 2.5 | / | 2.5 | / | 3 |
50 | / | 3.5 | / | 2.5 | / | 3 |
51 | / | 3 | / | 3 | / | 3 |
52 | / | 3 | / | 3 | / | 4 |
53 | / | 3.5 | / | 4 | / | 3 |
54 | / | 3.5 | / | 3.5 | / | 3.5 |
55 | / | 2.5 | / | 2.5 | / | 2.5 |
56 | / | 3 | / | 3 | / | 4 |
57 | / | 4 | / | 4.5 | / | 4 |
58 | / | 2.5 | / | 2.5 | / | 2.5 |
59 | / | 3 | / | 3 | / | 3 |
60 | / | 2.5 | / | 2.5 | / | 3.5 |
61 | 4.5 | 4 | 5.5 | 4 | 4.5 | 4 |
62 | 4 | 4 | 5 | 4.5 | 4.5 | 4.5 |
63 | 4.5 | 4.5 | 5 | 5 | 4.5 | 4.5 |
64 | 5 | 4.5 | 5.5 | 4.5 | 5 | 4.5 |
65 | 5.5 | 4.5 | 6 | 4 | 5.5 | 4.5 |
66 | 4.5 | 3 | 4.5 | 3 | 4 | 3.5 |
67 | 4.5 | 3.5 | 5.5 | 4 | 4.5 | 4.5 |
68 | 4 | 3.5 | 5 | 3 | 4.5 | 3.5 |
69 | 3.5 | 3 | 4 | 3 | 4.5 | 3.5 |
70 | 4 | 3.5 | 4.5 | 3 | 4.5 | 3.5 |
71 | 3.5 | 3.5 | 3.5 | 3.5 | 4 | 4 |
72 | 5.5 | 4 | 6 | 4.5 | 4 | 4 |
73 | 5 | 4 | 6 | 4 | 5 | 3.5 |
74 | 4.5 | 4 | 4.5 | 4.5 | 4.5 | 4 |
75 | 3.5 | 3 | 3.5 | 3 | 4 | 4 |
76 | 3 | 3 | 3.5 | 3 | 3.5 | 3.5 |
77 | 3 | 3.5 | 2.5 | 4 | 3.5 | 5 |
78 | 5 | 3.5 | 6 | 3.5 | 5 | 4 |
79 | 4.5 | 4 | 5.5 | 4 | 4.5 | 4 |
80 | 4 | 4 | 5 | 4 | 4.5 | 4 |
Scene | OT | OF | PT | PF | WLT | WLF | CT | CF |
---|---|---|---|---|---|---|---|---|
1 | / | 2.5 | / | 2.5 | / | 2 | / | 1.5 |
2 | / | 2.5 | / | 2.5 | / | 2.5 | / | 2.5 |
3 | / | 2.5 | / | 2.5 | / | 2.5 | / | 2.5 |
4 | / | 2.5 | / | 2.5 | / | 3 | / | 3 |
5 | / | 2.5 | / | 2.5 | / | 2 | / | 2 |
6 | / | 2.5 | / | 2.5 | / | 2.5 | / | 2 |
7 | / | 2 | / | 2.5 | / | 2.5 | / | 2 |
8 | / | 2.5 | / | 2.5 | / | 2.5 | / | 2 |
9 | / | 2 | / | 2 | / | 2.5 | / | 2 |
10 | / | 3 | / | 3 | / | 3 | / | 3 |
11 | / | 3 | / | 3 | / | 3.5 | / | 2.5 |
12 | / | 3 | / | 2.5 | / | 3.5 | / | 3 |
13 | / | 3 | / | 3 | / | 2.5 | / | 2 |
14 | / | 2.5 | / | 3 | / | 2.5 | / | 2 |
15 | / | 3 | / | 2.5 | / | 2.5 | / | 2 |
16 | / | 2.5 | / | 2.5 | / | 2.5 | / | 2.5 |
17 | / | 3 | / | 2.5 | / | 3 | / | 2.5 |
18 | / | 2.5 | / | 2.5 | / | 2.5 | / | 2 |
19 | / | 3 | / | 3 | / | 3 | / | 2.5 |
20 | / | 2.5 | / | 2.5 | / | 3 | / | 3 |
21 | 3 | 3 | 3 | 3 | 4 | 3.5 | 4 | 2.5 |
22 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
23 | 3 | 3 | 3 | 2.5 | 4 | 4 | 4 | 3.5 |
24 | 3.5 | 3.5 | 3 | 3 | 4 | 3.5 | 3.5 | 3.5 |
25 | 3.5 | 3.5 | 3.5 | 3 | 4 | 3.5 | 4.5 | 3.5 |
26 | 3 | 2.5 | 3 | 3 | 3.5 | 3 | 3.5 | 2.5 |
27 | 3.5 | 3.5 | 3.5 | 3 | 4.5 | 3.5 | 5 | 3.5 |
28 | 3.5 | 3 | 3.5 | 3 | 3.5 | 3.5 | 3.5 | 3 |
29 | 3 | 3 | 3 | 2.5 | 3 | 3 | 4 | 4.5 |
30 | 4 | 3.5 | 3.5 | 3.5 | 4 | 4 | 4 | 4.5 |
31 | 3 | 3 | 3 | 3 | 5 | 4 | 6 | 4 |
32 | 3.5 | 3.5 | 3.5 | 3.5 | 5 | 4.5 | 5 | 3.5 |
33 | 3.5 | 3 | 3.5 | 3 | 4 | 3.5 | 5.5 | 3 |
34 | 3 | 3 | 3 | 3 | 3.5 | 3.5 | 5.5 | 3.5 |
35 | 3.5 | 3.5 | 4 | 3.5 | 4.5 | 4 | 4.5 | 4.5 |
36 | 3 | 3 | 3.5 | 3 | 3.5 | 3.5 | 3.5 | 3.5 |
37 | 4 | 3.5 | 3.5 | 3.5 | 3.5 | 3.5 | 4.5 | 4.5 |
38 | 3.5 | 3.5 | 3.5 | 3.5 | 4 | 4 | 4.5 | 3.5 |
39 | 3.5 | 3 | 3 | 3 | 3.5 | 3.5 | 4 | 3 |
40 | 3.5 | 4.5 | 3.5 | 4.5 | 4 | 5 | 3.5 | 5 |
41 | / | 3 | / | 3 | / | 3 | / | 2 |
42 | / | 3 | / | 3 | / | 3 | / | 2 |
43 | / | 3 | / | 3 | / | 3 | / | 2.5 |
44 | / | 3 | / | 3 | / | 3 | / | 2.5 |
45 | / | 2.5 | / | 3 | / | 3 | / | 2.5 |
46 | / | 2.5 | / | 2.5 | / | 3 | / | 2.5 |
47 | / | 3 | / | 3 | / | 3 | / | 3 |
48 | / | 3 | / | 2.5 | / | 3 | / | 3 |
49 | / | 2.5 | / | 2.5 | / | 3 | / | 2.5 |
50 | / | 3 | / | 3 | / | 3 | / | 2.5 |
51 | / | 3.5 | / | 3 | / | 3.5 | / | 3 |
52 | / | 3 | / | 3 | / | 3.5 | / | 3 |
53 | / | 3.5 | / | 3.5 | / | 4 | / | 4 |
54 | / | 3.5 | / | 3.5 | / | 4 | / | 3.5 |
55 | / | 2.5 | / | 3 | / | 3 | / | 2.5 |
56 | / | 3 | / | 3 | / | 3.5 | / | 3 |
57 | / | 4 | / | 3.5 | / | 4 | / | 4.5 |
58 | / | 3 | / | 3 | / | 3 | / | 2.5 |
59 | / | 3.5 | / | 3.5 | / | 3.5 | / | 3 |
60 | / | 2.5 | / | 3 | / | 3.5 | / | 2.5 |
61 | 4 | 4 | 3.5 | 3.5 | 4.5 | 4 | 5.5 | 4 |
62 | 3.5 | 3 | 3.5 | 3.5 | 4.5 | 4.5 | 5 | 4.5 |
63 | 3.5 | 3.5 | 3.5 | 3 | 4 | 4 | 5 | 5 |
64 | 4.5 | 4 | 4 | 3.5 | 5 | 4.5 | 5.5 | 4.5 |
65 | 4 | 3.5 | 3.5 | 3 | 5 | 3.5 | 6 | 4 |
66 | 4 | 3 | 4 | 3 | 4 | 3 | 4.5 | 3 |
67 | 3.5 | 3.5 | 4 | 3.5 | 4.5 | 4 | 5.5 | 4 |
68 | 3.5 | 3.5 | 4 | 3.5 | 4.5 | 4 | 5 | 3 |
69 | 3 | 3 | 3 | 3 | 3.5 | 3.5 | 4 | 3 |
70 | 3.5 | 3.5 | 3.5 | 3 | 4 | 3.5 | 4.5 | 3 |
71 | 3.5 | 3 | 4 | 4 | 4 | 4 | 3.5 | 3.5 |
72 | 4 | 3.5 | 4.5 | 4 | 5 | 4 | 6 | 4.5 |
73 | 4.5 | 4 | 4 | 3 | 4.5 | 3.5 | 6 | 4 |
74 | 4 | 4 | 3.5 | 3.5 | 4.5 | 4 | 4.5 | 4.5 |
75 | 3.5 | 3 | 3.5 | 3 | 4 | 4 | 3.5 | 3 |
76 | 3 | 3 | 3 | 3 | 3.5 | 3 | 3.5 | 3 |
77 | 3 | 3.5 | 3.5 | 4 | 4 | 4.5 | 2.5 | 4 |
78 | 4 | 3.5 | 4 | 3 | 5 | 3.5 | 6 | 3.5 |
79 | 3.5 | 3.5 | 4 | 3 | 4 | 3 | 5.5 | 4 |
80 | 3.5 | 3.5 | 4 | 3.5 | 4.5 | 4 | 5 | 4 |
Scene | LBPT | LBPF | MNMIT | MNMIF | HCT | HCF | PT | PF |
---|---|---|---|---|---|---|---|---|
1 | / | 3 | / | 2.5 | / | 3 | / | 1.5 |
2 | / | 2 | / | 2.5 | / | 5.5 | / | 2.5 |
3 | / | 2.5 | / | 1.5 | / | 3 | / | 2.5 |
4 | / | 2 | / | 3 | / | 3 | / | 3 |
5 | / | 4 | / | 3 | / | 4 | / | 2 |
6 | / | 3.5 | / | 2.5 | / | 4.5 | / | 2 |
7 | / | 2 | / | 2 | / | 3.5 | / | 2 |
8 | / | 2 | / | 2 | / | 3.5 | / | 2 |
9 | / | 2.5 | / | 2.5 | / | 3 | / | 2 |
10 | / | 3.5 | / | 3.5 | / | 4 | / | 3 |
11 | / | 2.5 | / | 2 | / | 3.5 | / | 2.5 |
12 | / | 2 | / | 3.5 | / | 3.5 | / | 3 |
13 | / | 3.5 | / | 3 | / | 4.5 | / | 2 |
14 | / | 3 | / | 3.5 | / | 4.5 | / | 2 |
15 | / | 2 | / | 1.5 | / | 4 | / | 2 |
16 | / | 2 | / | 2 | / | 3.5 | / | 2.5 |
17 | / | 3 | / | 3.5 | / | 3.5 | / | 2.5 |
18 | / | 3.5 | / | 2 | / | 4 | / | 2 |
19 | / | 2 | / | 2.5 | / | 4 | / | 2.5 |
20 | / | 2.5 | / | 3 | / | 3.5 | / | 3 |
21 | 0 | 3 | 2 | 3 | 3.5 | 3.5 | 4 | 2.5 |
22 | 2 | 2 | 3 | 3 | 2 | 4 | 3 | 3 |
23 | 4 | 2 | 2.5 | 2 | 0 | 5.5 | 4 | 3.5 |
24 | 3 | 3 | 2.5 | 2.5 | 3 | 4 | 3.5 | 3.5 |
25 | 0 | 4 | 0.5 | 3 | 4.5 | 4 | 4.5 | 3.5 |
26 | 0 | 3 | 0.5 | 4 | 1.5 | 4.5 | 3.5 | 2.5 |
27 | 2.5 | 2.5 | 3.5 | 3 | 2.5 | 4 | 5 | 3.5 |
28 | 2.5 | 3 | 3.5 | 2 | 2.5 | 4 | 3.5 | 3 |
29 | 2 | 3.5 | 3 | 3 | 3 | 5 | 4 | 4.5 |
30 | 1.5 | 4.5 | 0 | 3.5 | 3.5 | 5 | 4 | 4.5 |
31 | 4 | 3 | 5 | 1.5 | 3.5 | 4 | 6 | 4 |
32 | 3.5 | 2.5 | 3.5 | 3.5 | 4 | 5 | 5 | 3.5 |
33 | 2.5 | 4 | 3.5 | 3.5 | 3.5 | 4.5 | 5.5 | 3 |
34 | 2 | 4.5 | 3.5 | 4 | 4 | 4 | 5.5 | 3.5 |
35 | 1.5 | 3.5 | 2 | 2.5 | 3.5 | 5 | 4.5 | 4.5 |
36 | 2 | 2 | 3 | 2.5 | 3.5 | 4 | 3.5 | 3.5 |
37 | 0 | 3 | 1 | 4.5 | 2.5 | 4 | 4.5 | 4.5 |
38 | 0 | 4 | 0.5 | 2.5 | 4 | 4 | 4.5 | 3.5 |
39 | 2 | 2.5 | 3 | 3 | 3 | 4 | 4 | 3 |
40 | 2.5 | 3 | 3 | 3.5 | 3.5 | 4 | 3.5 | 5 |
41 | / | 4 | / | 3 | / | 4 | / | 2 |
42 | / | 4 | / | 3.5 | / | 5.5 | / | 2 |
43 | / | 4.5 | / | 3 | / | 4.5 | / | 2.5 |
44 | / | 4 | / | 3 | / | 5.5 | / | 2.5 |
45 | / | 4 | / | 3 | / | 4.5 | / | 2.5 |
46 | / | 4 | / | 3 | / | 6 | / | 2.5 |
47 | / | 4 | / | 3 | / | 3 | / | 3 |
48 | / | 5 | / | 3.5 | / | 3.5 | / | 3 |
49 | / | 4 | / | 3 | / | 5 | / | 2.5 |
50 | / | 4.5 | / | 3 | / | 4 | / | 2.5 |
51 | / | 4 | / | 3 | / | 3.5 | / | 3 |
52 | / | 4.5 | / | 3.5 | / | 3.5 | / | 3 |
53 | / | 3 | / | 3 | / | 5 | / | 4 |
54 | / | 5 | / | 3 | / | 5 | / | 3.5 |
55 | / | 4 | / | 3 | / | 4 | / | 2.5 |
56 | / | 5 | / | 3 | / | 3.5 | / | 3 |
57 | / | 3.5 | / | 3 | / | 3 | / | 4.5 |
58 | / | 2.5 | / | 3 | / | 4 | / | 2.5 |
59 | / | 3 | / | 3 | / | 4 | / | 3 |
60 | / | 4.5 | / | 3.5 | / | 3.5 | / | 2.5 |
61 | 4.5 | 4.5 | 4.5 | 2.5 | 3 | 4 | 5.5 | 4 |
62 | 2 | 4 | 3 | 4 | 4.5 | 4 | 5 | 4.5 |
63 | 2.5 | 4.5 | 3 | 4 | 2 | 4.5 | 5 | 5 |
64 | 2 | 4 | 3 | 4 | 4 | 4 | 5.5 | 4.5 |
65 | 5 | 4.5 | 5 | 3.5 | 1.5 | 4.5 | 6 | 4 |
66 | 4 | 4 | 4 | 4 | 4.5 | 4.5 | 4.5 | 3 |
67 | 4 | 4 | 3.5 | 3.5 | 1.5 | 4 | 5.5 | 4 |
68 | 4 | 4 | 3.5 | 3.5 | 5.5 | 4.5 | 5 | 3 |
69 | 3.5 | 3.5 | 3 | 4 | 4 | 5 | 4 | 3 |
70 | 1.5 | 4 | 3 | 3.5 | 4 | 4 | 4.5 | 3 |
71 | 3.5 | 3.5 | 4 | 4 | 5.5 | 5 | 3.5 | 3.5 |
72 | 4 | 4 | 4 | 4 | 5.5 | 4.5 | 6 | 4.5 |
73 | 3.5 | 3.5 | 4.5 | 4.5 | 3 | 4 | 6 | 4 |
74 | 4.5 | 4.5 | 3.5 | 3.5 | 6 | 5 | 4.5 | 4.5 |
75 | 4 | 4 | 4 | 4 | 4 | 4 | 3.5 | 3 |
76 | 4 | 4 | 4.5 | 4.5 | 4 | 4 | 3.5 | 3 |
77 | 4 | 4 | 3.5 | 3.5 | 4 | 4 | 2.5 | 4 |
78 | 4 | 2 | 3.5 | 4.5 | 4.5 | 3.5 | 6 | 3.5 |
79 | 3.5 | 4 | 3.5 | 3.5 | 4.5 | 4.5 | 5.5 | 4 |
80 | 3.5 | 4 | 4 | 4 | 3.5 | 4.5 | 5 | 4 |
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Bao, Q.; Wang, Y.; Hua, H.; Dong, K.; Lee, F. An Anti-Forensics Video Forgery Detection Method Based on Noise Transfer Matrix Analysis. Sensors 2024, 24, 5341. https://doi.org/10.3390/s24165341
Bao Q, Wang Y, Hua H, Dong K, Lee F. An Anti-Forensics Video Forgery Detection Method Based on Noise Transfer Matrix Analysis. Sensors. 2024; 24(16):5341. https://doi.org/10.3390/s24165341
Chicago/Turabian StyleBao, Qing, Yagang Wang, Huaimiao Hua, Kexin Dong, and Feifei Lee. 2024. "An Anti-Forensics Video Forgery Detection Method Based on Noise Transfer Matrix Analysis" Sensors 24, no. 16: 5341. https://doi.org/10.3390/s24165341
APA StyleBao, Q., Wang, Y., Hua, H., Dong, K., & Lee, F. (2024). An Anti-Forensics Video Forgery Detection Method Based on Noise Transfer Matrix Analysis. Sensors, 24(16), 5341. https://doi.org/10.3390/s24165341