Authentication of Video Evidence for Forensic Investigation: A Case of Nigeria

Video shreds of evidence are usually admissible in the court of law all over the world. However, individuals manipulate these videos to either defame or incriminate innocent people. Others indulge in video tampering to falsely es-cape the wrath of the law against misconducts. One way impostors can forge these videos is through inter-frame video forgery. Thus, the integrity of such videos is under threat. This is because these digital forgeries seriously debase the credibility of video contents as being definite records of events. This leads to an increasing concern about the trustworthiness of video contents. Hence, it continues to affect the social and legal system, forensic investigations, intel-ligence services, and security and surveillance systems as the case may be. The problem of inter-frame video forgery is increasingly spontaneous as more video-editing software continues to emerge. These video editing tools can easily manipulate videos without leaving obvious traces and these tampered videos become viral. Alarmingly, even the beginner users of these editing tools can alter the contents of digital videos in a manner that renders them practically indistinguishable from the original content by mere observations. This paper, however, leveraged on the concept of correlation coefficients to produce a more elaborate and reliable inter-frame video detection to aid forensic investigations, especially in Nigeria. The model employed the use of the idea of a threshold to efficiently distinguish forged videos from authentic videos. A benchmark and locally manipulated video datasets were used to evaluate the proposed model. Experimentally, our approach performed better than the existing methods. The overall accuracy for all the evaluation metrics such as accuracy, recall, precision and F1-score was 100%. The proposed method implemented in the MATLAB programming language has proven to effectively detect inter-frame forgeries.


Introduction
Video forgeries continue to be a great challenge to the admissibility of video evidence in the court of law, especially in Nigeria.
Due to the rapid increase in highly sophisticated video-editing software and video tampering techniques, there are several kinds of tampering techniques such as splicing, resampling, adding and or removing a portion from a video clip to mention but a few [1]. Inter-frame video forgery is the removal or insertion of a set of frames from or into a video [2]. Such practices affect the sequence of frames in a video. But video editing tools such as Adobe Premiere and Pinnacle Studio make video forgeries easier. Thus, causing serious threats, defaming and reducing the credibility of digital contents. Considering that videos can be used as evidence in the court of law or as news items, there is a need for authentication of these videos. Therefore, it has become imperative to carry out more research in the exploration for better ways to avert the bothersome level of digital video content manipulations. With very few forensic experts in Nigeria tasked with the huge responsibility of authenticating videos for forensic purposes, there is a need to develop novel approaches to tackle video forgeries.
Thus, this paper proposes a novel inter-frame video forgery detection method based on adjacent frames and has effectively detected video forgeries for forensic purposes. The rest of the paper is organised as follows. Related works are described in Section 2. Section 3 explains the materials and methods. Results and discussion are presented and discussed in Section 4. Conclusion and future work are presented in Section 5.

Related Works
Considerably, many forensics techniques have been proposed. Chao, Jiang and Sun [3], developed an inter-frame video forgery detection model using Lucas Kanade optical flow. The model used window based and binary search based detection for frame inserted forgery detection. A double adaptive threshold was used to detect the differences in the optical flow. The model achieved 95% recall and 98% precision evaluation results. Frame deleted forgery detection achieved 85% and 89% recall and precision, respectively. Wang, Li, Zhang and Ma [4], leveraged on the concept of the consistency of correlation coefficients to classify inter-frame video tampered videos from orig-  Evaluation results demonstrated that the model outperforms other methods especially the inter-frame shuffling detection at that time.

Selected Cases of Video Evidence/Manipulations in Nigeria
In 2013, a video of a lecturer at Delta State University, Abraka, went viral. In the clip, the female student tactically captured the lecturer naked. Sources revealed that the lecturer had failed the female student in the second year for rejecting his sexual request. Until in the final year, the lecturer insisted on having sex with her first, so she cajoled him to her apartment, and captured him naked to serve as evidence [14].
A video went viral in 2018, indicating a scenario where a governor was receiving what was tagged a "kickback" meaning a return bribe from a contractor.
The video showed that the governor received bundles of dollars from the contractor. In the video, the governor had requested 5 million US dollars from the contractor who recorded the video while handing in part of the payment. Most evidently, the uploaded version of the video was without audio content. Seemingly to conceal some details [15].
Again, Mwai [16], confirmed another misleading video clip during the End This calls for forensic experts to devise techniques or means of authenticating digital contents to restore the lost trust in digitally generated contents [17].

Materials and Methods
Two datasets are used in this experiment. The first dataset is from VIFFD [18].
where: f(l) = frame label; M = Manipulation due to deletion/insertion; N = Normal or original video; no ({f(l)}) = number of deleted/inserted frames; tdb = threshold to determine insertion/deletion vs normal frames. In our case, the tdb is set to 3.4.

Results and Discussion
The benchmark tests/results are carried out using VIFFD-A dataset for detecting video inter-frame forgeries (Nguyen, and Hu, 2020). This serves as a benchmark/standard upon which the locally manipulated videos were measured. Our source code was written in MATLAB and can be downloaded from

Evaluation Metrics Used
This study adopts the use of Recall, Precision, Accuracy, F 1 -score and Confusion Matrix for evaluation. This is necessary to ascertain the effectiveness and efficiency of the proposed approach over other existing state-of-the-art approaches.
The formulae for each of the evaluation measures are given as follows:  Figure 1 shows the frames extracted from the benchmark original video.

Detection Performance of Original, Frame Inserted and Frame Deleted Videos Using the Benchmark Datasets
The correlation coefficients of the benchmark original videos in Figure 1 are as shown in Figure 2.
We observe that the differences in the frames are stable, hence the correlation coefficients of the benchmark original frames are consistent as shown in Figure 2. Figure 3 and Figure 4 depict the benchmark results for insertion forgery.

Frame Inserted-Benchmark Result
The inconsistency in the frames based on frame insertion are detected and   shown in Figure 3. Its correlation coefficients is shown in Figure 4. Figure 4 validates the claim that a frame was inserted at a location around frames 70 and 80 as detected in Figure 3. Figure 5 and Figure 6 shows the benchmark results for deletion forgery.

Frame Deleted-Benchmark Result
In Figure 5, the inconsistency occurred in the first frame. The frame containing the picture of a person was removed.

Frame Inserted Results-Locally Made Datasets
Usually, the frame inserted videos are discontinuous and inconsistent. The following four frames in Figure 8 depict this scenario.
From Figure 8, the second frame which is at the top right corner shows the discontinuity/inconsistency of a frame inserted in the video.
The correlation coefficients of the detected frame inserted video are shown in Figure 9. In Figure 9, the differences in the frames are noticeably unstable and inconsistent. The region between frames 80 and 100 indicates the point at which a frame was introduced, added or inserted into the original video data. Thus, the changes in the correlation coefficients suddenly rise to the peak at the point a frame was inserted.

Frame Deleted Results-Locally Made Datasets
In the frame deleted video data as shown in Figure 10, the changes in the frames are not as obvious as in the frame inserted video forgery. However, the inconsistency in the correlation coefficients of the frame deleted video data detects and  The correlation coefficients of the frame deleted video are as shown in Figure   11.
From Figure 11, the inconsistency became obvious at a point between frames 200 and 300. The spikes at this point became very high. Hence, this indicates the point at which a frame was deleted or cut off from the original video data.

Detection Performance for Deletion Forgery
The confusion matrix in Figure 12 shows the result for 3 original videos and 3 Figure 11. Correlation coefficients of a frame deleted video. From the confusion matrix in Figure 12, all the deleted frames are detected with an overall accuracy of 100%. Table 1 compares our proposed model with other similar research works based on recall, precision, accuracy and F1-score for deletion detection.
From the comparison, we observed that our proposed model outperformed the previous closely related deletion detection methods.
This means that our proposed method can effectively assist law enforcement agencies in Nigeria to investigate and authenticate videos that could be presented and used in the court of law especially when checking for deletion forgeries detection.

Detection Performance for Insertion Forgery
For insertion forgery detection, the confusion matrix is shown in Figure 13 for 3 original videos and 4 insertion videos.
From Figure 13, it can be observed that the overall accuracy of the model returned 100% accuracy.

Conclusion and Future Work
This study leveraged on the consistency of correlation coefficients. Hence, classified original videos and tampered videos. The model adopts the use of the threshold technique for effective detection. The model demonstrates a simple but powerful process of verifying the authenticity of inter-frame video forgeries.
With this approach, digital contents associated with frame insertion or deletion in situations like the recent End SARS protest in Nigeria can be detected to ascertain their originality. That is, the model can detect whether a digital video on social media is genuine or not without prior knowledge of the original video data. Experimental results using the VIFFD and our locally manipulated video data indicate that the proposed model performed better than the existing methods. The overall accuracy for all the evaluation metrics such as recall, precision and F1-score achieved 100% accuracy.
In our future work, we plan to investigate the detection of more video forgeries using our proposed model. Additionally, machine learning techniques will be adopted to detect more video forgeries.