Exposing Digital Forgeries in Ballistic Motion

Conotter, Valentina; O’Brien, James F.; Farid, Hany

doi:10.1109/tifs.2011.2165843

Cited by 56 publications

(26 citation statements)

References 19 publications

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“…Detection of staged video involving natural ballistic trajectories is examined in [32], and [19] details how plausible audiovisual personation is achieved in front of the camera. Digital video forgery can take a number of forms [14] and Figure 1 gives an overview of the classical interpretation.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

A review of digital video tampering: From simple editing to full synthesis

Johnston

Elyan

2019

Digital Investigation

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Video tampering methods have witnessed considerable progress in recent years. This is partly due to the rapid development of advanced deep learning methods, and also due to the large volume of video footage that is now in the public domain. Historically, convincing video tampering has been too labour intensive to achieve on a large scale. However, recent developments in deep learning-based methods have made it possible not only to produce convincing forged video but also to fully synthesize video content. Such advancements provide new means to improve visual content itself, but at the same time, they raise new challenges for state-of-the-art tampering detection methods. Video tampering detection has been an active field of research for some time, with periodic reviews of the subject. However, little attention has been paid to video tampering techniques themselves. This paper provides an objective and in-depth examination of current techniques related to digital video manipulation. We thoroughly examine their development, and show how current evaluation techniques provide opportunities for the advancement of video tampering detection. A critical and extensive review of photo-realistic video synthesis is provided with emphasis on deep learning-based methods. Existing tampered video datasets are also qualitatively reviewed and critically discussed. Finally, conclusions are drawn upon an exhaustive and thorough review of tampering methods with

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Section: Accepted Manuscriptmentioning

confidence: 99%

A review of digital video tampering: From simple editing to full synthesis

Johnston

Elyan

2019

Digital Investigation

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“…Another novel concept was presented in [95], where manipulated videos were identified by detecting physically improbable trajectories of solid objects in the video. The objective was to create a 3D model of the parabolic trajectories of objects in free flight (for instance, the trajectory of a basketball being thrown towards the basket) and its corresponding 2D projection onto the image plane, and then weed out inconsistencies from a geometric point of view.…”

Section: Object Features Based Copy-paste Detection Techniquesmentioning

confidence: 99%

Video content authentication techniques: a comprehensive survey

Singh

Aggarwal

2017

Multimedia Systems

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“…In this context, we demonstrate that suitable physical models are powerful regularizers for underdetermined problems. Conotter et al [2012] analyze the kinematics of the ballistic motion of a single object in videos in the digital forensics context. Salzmann and Urtasun [2011] explain the change in the linear motion of objects by solving for a sparse set of forces, applying their dynamics motivated regularization in video based tracking.…”

Section: Related Workmentioning

confidence: 99%

Smash

2016

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Figure 1: Starting from an input video of a collision sequence behind a curtain (top), SMASH reconstructs an accurate physically valid collision (bottom) using laws of rigid body physics for regularization. Note the reconstructed spin (i.e., angular velocity) of the objects. AbstractCollision sequences are commonly used in games and entertainment to add drama and excitement. Authoring even two body collisions in the real world can be difficult, as one has to get timing and the object trajectories to be correctly synchronized. After tedious trial-and-error iterations, when objects can actually be made to collide, then they are difficult to capture in 3D. In contrast, synthetically generating plausible collisions is difficult as it requires adjusting different collision parameters (e.g., object mass ratio, coefficient of restitution, etc.) and appropriate initial parameters. We present SMASH to directly read off appropriate collision parameters directly from raw input video recordings. Technically we enable this by utilizing laws of rigid body collision to regularize the problem of lifting 2D trajectories to a physically valid 3D reconstruction of the collision. The reconstructed sequences can then be modified and combined to easily author novel and plausible collisions. We evaluate our system on a range of synthetic scenes and demonstrate the effectiveness of our method by accurately reconstructing several complex real world collision events.

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Exposing Digital Forgeries in Ballistic Motion

Cited by 56 publications

References 19 publications

A review of digital video tampering: From simple editing to full synthesis

A review of digital video tampering: From simple editing to full synthesis

Video content authentication techniques: a comprehensive survey

Smash

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