Authentication of Surveillance Videos: Detecting Frame Duplication Based on Residual Frame

Fadl, Sondos M.; Han, Qi; Li, Qiong

doi:10.1111/1556-4029.13658

Cited by 29 publications

(12 citation statements)

References 18 publications

(50 reference statements)

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“…As most of the public data sets available are meant for intra-frame regional forgery, we simulated an inter-frame forged data set from publicly available videos. The frame-level forgery was generated by inserting/deleting/duplicating a random sequence of frames as [12][13][14][15][16][17][18] did. The original videos used for tampering creation were mostly taken from a public data set DERF (https://media.xiph.org/ video/derf/) and some of them from YouTube.…”

Section: Data Setmentioning

confidence: 99%

Inter‐frame forgery detection and localisation in videos using earth mover's distance metric

Selvaraj¹,

Muneeswaran²

2020

IET image process

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Video forensics is one of the hot topics in multimedia forensics. Nowadays, spreading fake videos across the internet is becoming a profession mainly in politics and entertainment. In this study, a novel two-stage inter-frame video forgery detection technique is proposed. The first stage analyses spatio-temporal feature flow consistency to detect suspicious tamper points. Identifying the type of forgery and validating the recovered video are done in the second stage. Earth mover's distance is used as a similarity metric in both stages. The authors concentrate on a robust inter-frame forgery detection approach which can be applied for any challenging video. Compression at a higher rate, noise addition, and filtering are the anti-forensic tricks used by forgers to fool forensic techniques. However, most of the literature in video forgery detection has handled these issues as postprocessing attacks and reported lesser accuracies for it. Hence the authors propose a robust and efficient forgery detection technique capable of identifying all kinds of inter-frame forgeries in videos. Experimental evaluation of the public video data set shows that the proposed approach outperforms existing approaches with an improved rate of robustness. 10 IET Image Process.

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Section: Data Setmentioning

confidence: 99%

Inter‐frame forgery detection and localisation in videos using earth mover's distance metric

Selvaraj¹,

Muneeswaran²

2020

IET image process

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“…Recent studies have indicated that detecting the frame duplication attack in a static scene is difficult by comparing the objects in replayed motion. 20 The difficulty is partly due to the noise interference of the camera over its video output, as well as the changes in the light intensity caused by indoor lighting or natural ambient light. To exploit the limitations of existing detection techniques and human perception of live video streams, in our attack, the compromised surveillance camera retains a recently recorded static scene for masking live stream whenever triggered.…”

Section: Video-audio Replay Attackmentioning

confidence: 99%

A study on smart online frame forging attacks against Video Surveillance System

Nagothu

Schwell

Chen

et al. 2019

Sensors and Systems for Space Applications XII

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Video Surveillance Systems (VSS) have become an essential infrastructural element of smart cities by increasing public safety and countering criminal activities. A VSS is normally deployed in a secure network to prevent the access from unauthorized personnel. Compared to traditional systems that continuously record video regardless of the actions in the frame, a smart VSS has the capability of capturing video data upon motion detection or object detection, and then extracts essential information and send to users. This increasing design complexity of the surveillance system, however, also introduces new security vulnerabilities. In this work, a smart, real-time frame duplication attack is investigated. We show the feasibility of forging the video streams in real-time as the camera's surroundings change. The generated frames are compared constantly and instantly to identify changes in the pixel values that could represent motion detection or changes in light intensities outdoors. An attacker (intruder) can remotely trigger the replay of some previously duplicated video streams manually or automatically, via a special quick response (QR) code or when the face of an intruder appear in the camera field of view. A detection technique is proposed by leveraging the real-time electrical network frequency (ENF) reference database to match with the power grid frequency.

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“…These algorithms mostly extract features from a video sub-sequence and compare them with other sub-sequences for similarity [16]. A number of correlation techniques [6,17,18] have also been adopted to identify frame duplication and region duplication in a video. All these similarity detection techniques require a stored surveillance recording database, and hence they require much computation time to process each video frame.…”

Section: Background Knowledge and Related Workmentioning

confidence: 99%

“…Here, we opted to deploy the attack once a static scene appeared again instead of immediately launching the attack. Deploying the attack with static scene avoids suspicious artifacts like the sudden disappearance of a person from frame, and detecting duplicated frames in a static scene is harder than frames with objects in motion [18]. In Figure 6b, the periodic changes in the environment is reflected in the replay recording.…”

Section: Real-time Frame Duplication Attack Implementationmentioning

confidence: 99%

Detecting Malicious False Frame Injection Attacks on Surveillance Systems at the Edge Using Electrical Network Frequency Signals

Nagothu

Chen

Blasch

et al. 2019

Sensors

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Over the past few years, the importance of video surveillance in securing national critical infrastructure has significantly increased, with applications including the detection of failures and anomalies. Accompanied by the proliferation of video is the increasing number of attacks against surveillance systems. Among the attacks, False Frame Injection (FFI) attacks that replay video frames from a previous recording to mask the live feed has the highest impact. While many attempts have been made to detect FFI frames using features from the video feeds, video analysis is computationally too intensive to be deployed on-site for real-time false frame detection. In this paper, we investigated the feasibility of FFI attacks on compromised surveillance systems at the edge and propose an effective technique to detect the injected false video and audio frames by monitoring the surveillance feed using the embedded Electrical Network Frequency (ENF) signals. An ENF operates at a nominal frequency of 60 Hz/50 Hz based on its geographical location and maintains a stable value across the entire power grid interconnection with minor fluctuations. For surveillance system video/audio recordings connected to the power grid, the ENF signals are embedded. The time-varying nature of the ENF component was used as a forensic application for authenticating the surveillance feed. The paper highlights the ENF signal collection from a power grid creating a reference database and ENF extraction from the recordings using conventional short-time Fourier Transform and spectrum detection for robust ENF signal analysis in the presence of noise and interference caused in different harmonics. The experimental results demonstrated the effectiveness of ENF signal detection and/or abnormalities for FFI attacks.

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Authentication of Surveillance Videos: Detecting Frame Duplication Based on Residual Frame

Cited by 29 publications

References 18 publications

Inter‐frame forgery detection and localisation in videos using earth mover's distance metric

Inter‐frame forgery detection and localisation in videos using earth mover's distance metric

A study on smart online frame forging attacks against Video Surveillance System

Detecting Malicious False Frame Injection Attacks on Surveillance Systems at the Edge Using Electrical Network Frequency Signals

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