An improved surveillance video forgery detection technique using sensor pattern noise and correlation of noise residues

Fayyaz, Muhammad; Anjum, Adeel; Ziauddin, Sheikh; Khan, Arshad; Sarfaraz, Aaliya

doi:10.1007/s11042-019-08236-2

Cited by 23 publications

(15 citation statements)

References 32 publications

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“…Traditional watermark-based approaches require dedicated modules for video integrity preservation, while not all commercial cameras have such a watermarking module. Passive video forensics approaches leverage video statistic characteristics to discover forgery traces [4], [5], [6], [7], [8]. However, such approaches are computationallyexpensive and cannot be applied for real-time forgery detection on live video feeds.…”

Section: Resultsmentioning

confidence: 99%

“…Traditional watermark-based approaches require dedicated modules on surveillance cameras for video integrity preservation, while not all camera manufacturers support such modules. Alternatively, many video forensics approaches that exploit statistic characteristics of video signals [4], [5], [6], [7], [8] are developed to detect tampered frames and further localize forged regions in them. Though possessing finegrained detection abilities, these approaches basically rely on various spatial-temporal analysis methods, which require a high computational complexity, and therefore are ill-suited for real-time attack detection on live surveillance videos.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos

Huang

Wang

et al. 2021

Preprint

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The cybersecurity breaches render surveillance systems vulnerable to video forgery attacks, under which authentic live video streams are tampered to conceal illegal human activities under surveillance cameras. Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds. The recent work correlates time-series camera and wireless signals to recognize replayed surveillance videos using event-level timing information but it cannot realize fine-grained forgery detection and localization on each frame. To fill this gap, this paper proposes Secure-Pose, a novel cross-modal forgery detection and localization system for live surveillance videos using WiFi signals near the camera spot. We observe that coexisting camera and WiFi signals convey common human semantic information and the presence of forgery attacks on video frames will decouple such information correspondence. Secure-Pose extracts effective human pose features from synchronized multi-modal signals and detects and localizes forgery traces under both inter-frame and intra-frame attacks in each frame. We implement Secure-Pose using a commercial camera and two Intel 5300 NICs and evaluate it in real-world environments. Secure-Pose achieves a high detection accuracy of 95.1% and can effectively localize tampered objects under different forgery attacks.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos

Huang

Wang

et al. 2021

Preprint

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show abstract

“…Lastly, a threshold is selected to identify the tampering. Fayyaz et al [113] developed a video tampering detection method based on sensor noise patterns of video frames. The noise patterns were extracted using denoising video frames; latterly, noise patterns were averaged to detect sensor noise patterns.…”

Section: Methods Based On Camera Sourcementioning

confidence: 99%

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

et al. 2022

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Digital videos are now low-cost, easy to capture and easy to share on social media due to the common feature of video recording in smart phones and digital devices. However, with the advancement of video editing tools, videos can be tampered (forged) easily for propaganda or to gain illegal advantages—ultimately, the authenticity of videos shared on social media cannot be taken for granted. Over the years, significant research has been devoted to developing new techniques for detecting different types of video tampering. In this paper, we offer a detailed review of existing passive video tampering detection techniques in a systematic way. The answers to research questions prepared for this study are also elaborated. The state-of-the-art research work is analyzed extensively, highlighting the pros and cons and commonly used datasets. Limitations of existing video forensic algorithms are discussed, and we conclude with research challenges and future directions.

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“…Video Forgery Detection. Passive video forensics approaches leverage video statistic characteristics to discover forgery traces [6], [7], [8], [9], [10]. However, such approaches generally rely on relatively long video clips and are illsuited for live video feeds.…”

Section: Related Workmentioning

confidence: 99%

“…Although extensive efforts have been devoted to defending against video forgery attacks, existing approaches still fall short in achieving both real-time forgery detection and finegrained forgery localization. Traditional watermark-based approaches require dedicated modules on security cameras for video integrity preservation, while not all camera manufacturers support such modules [6]. Alternatively, many video forensics approaches that exploit video statistic characteristics [7], [8], [9], [10] are developed to detect tampered frames and further localize forgery traces.…”

Section: Introductionmentioning

confidence: 99%

Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information

Huang

Wang

et al. 2022

Preprint

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The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities. Traditional video forensics approaches can localize forgery traces using spatialtemporal analysis on relatively long video clips, while falling short in real-time forgery detection. The recent work correlates time-series camera and wireless signals to detect looped videos but cannot realize fine-grained forgery localization. To overcome these limitations, we propose Secure-Pose, which exploits the pervasive coexistence of surveillance and Wi-Fi infrastructures to defend against video forgery attacks in a real-time and finegrained manner. We observe that coexisting camera and Wi-Fi signals convey common human semantic information and forgery attacks on video streams will decouple such information correspondence. Particularly, retrievable human pose features are first extracted from concurrent video and Wi-Fi channel state information (CSI) streams. Then, a lightweight detection network is developed to accurately discover forgery attacks and an efficient localization algorithm is devised to seamlessly track forgery traces in video streams. We implement Secure-Pose using one Logitech camera and two Intel 5300 NICs and evaluate it in different environments. Secure-Pose achieves a high detection accuracy of 98.7% and localizes abnormal objects under playback and tampering attacks.

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An improved surveillance video forgery detection technique using sensor pattern noise and correlation of noise residues

Cited by 23 publications

References 32 publications

Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos

Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information

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