Deleting Object in Video Copy-Move Forgery Detection Based on Optical Flow Concept

Al-Sanjary, Omar Ismael; Ahmed, Ahmed A.; Ahmad, Hawar B.; Ali, Musab A. M.; Mohammed, M. N.; Abdullah, Muhammad Irsyad; Ishak, Zurida

doi:10.1109/spc.2018.8704160

Cited by 13 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optical flow represents the pattern of apparent motion of an image between consecutive frames and its displacement. Using the feature vector designed from the optical flow, copy-move forgery can be identified [31]. Features are generated for each frame and then lexicographically sorted [57].…”

Section: Conventional Detection Methodsmentioning

confidence: 99%

“…Video recordings used for temporal correlation of the live events are primarily targeted using frame shuffling or duplication attacks [30]. The perception of live events is affected, which disables the effectiveness of live monitoring [31]. Adaptive replay attacks are designed such that the frame duplication attack can adapt to the changes in the environments such as light intensity variations, object displacement, and camera alignments.…”

Section: Frame Manipulation Attacksmentioning

confidence: 99%

See 1 more Smart Citation

Evolution of Attacks on Intelligent Surveillance Systems and Effective Detection Techniques

Nagothu¹,

Poredi²,

Chen³

2023

Intelligent Video Surveillance - New Perspectives

View full text Add to dashboard Cite

Intelligent surveillance systems play an essential role in modern smart cities to enable situational awareness. As part of the critical infrastructure, surveillance systems are often targeted by attackers aiming to compromise the security and safety of smart cities. Manipulating the audio or video channels could create a false perception of captured events and bypass detection. This chapter presents an overview of the attack vectors designed to compromise intelligent surveillance systems and discusses existing detection techniques. With advanced machine learning (ML) models and computing resources, both attack vectors and detection techniques have evolved to use ML-based techniques more effectively, resulting in non-equilibrium dynamics. The current detection techniques vary from training a neural network to detect forgery artifacts to use the intrinsic and extrinsic environmental fingerprints for any manipulations. Therefore, studying the effectiveness of different detection techniques and their reliability against the defined attack vectors is a priority to secure the system and create a plan of action against potential threats.

show abstract

Section: Conventional Detection Methodsmentioning

confidence: 99%

Section: Frame Manipulation Attacksmentioning

confidence: 99%

Evolution of Attacks on Intelligent Surveillance Systems and Effective Detection Techniques

Nagothu¹,

Poredi²,

Chen³

2023

Intelligent Video Surveillance - New Perspectives

View full text Add to dashboard Cite

show abstract

“…Moreover, this technique was unable to localize the forged regions. Al-Sanjary et al [107] exploited inconsistency in optical flow to detect and localize the copy-move forged region. This study used nine videos to test the method and achieved 96% accuracy.…”

Section: Methods Based On Pixels and Texture Featuresmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

“…This approach has the disadvantage of being computationally expensive and complex. Al-Sanjary et al [8] estimated the optical flow using two successive frame features. In this technique, pixel-and block-based estimation is undertaken to detect copy-move forgery videos.…”

Section: Spatial Domain Forgery Detectionmentioning

confidence: 99%

A survey on passive digital video forgery detection techniques

Jegaveerapandian,

Rani,

Periyaswamy

et al. 2023

IJECE

View full text Add to dashboard Cite

Digital media devices such as smartphones, cameras, and notebooks are becoming increasingly popular. Through digital platforms such as Facebook, WhatsApp, Twitter, and others, people share digital images, videos, and audio in large quantities. Especially in a crime scene investigation, digital evidence plays a crucial role in a courtroom. Manipulating video content with high-quality software tools is easier, which helps fabricate video content more efficiently. It is therefore necessary to develop an authenticating method for detecting and verifying manipulated videos. The objective of this paper is to provide a comprehensive review of the passive methods for detecting video forgeries. This survey has the primary goal of studying and analyzing the existing passive techniques for detecting video forgeries. First, an overview of the basic information needed to understand video forgery detection is presented. Later, it provides an in-depth understanding of the techniques used in the spatial, temporal, and spatio-temporal domain analysis of videos, datasets used, and their limitations are reviewed. In the following sections, standard benchmark video forgery datasets and the generalized architecture for passive video forgery detection techniques are discussed in more depth. Finally, identifying loopholes in existing surveys so detecting forged videos much more effectively in the future are discussed.

show abstract

Deleting Object in Video Copy-Move Forgery Detection Based on Optical Flow Concept

Cited by 13 publications

References 23 publications

Evolution of Attacks on Intelligent Surveillance Systems and Effective Detection Techniques

Evolution of Attacks on Intelligent Surveillance Systems and Effective Detection Techniques

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

A survey on passive digital video forgery detection techniques

Contact Info

Product

Resources

About