A framework for estimating geometric distortions in video copies based on visual-audio fingerprints

Roopalakshmi, R.; Reddy, G. Ram Mohana

doi:10.1007/s11760-013-0424-7

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…ST&V hash is the XOR operation between ST and V hashes. This fingerprint is robust against some temporal attacks (frame dropping, frame rate conversion, and frame exchanging) and signal processing attacks (Gaussian noising, median filtering, histogram equalization, among others).CST-SURF [27]. CST-SURF is part of a spatiotemporal registration framework.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, in [20] four detectors are combined: SIFT, SURF, DCT for visual component and WASF for audio component. Other methods that combine visual and acoustic information are proposed in [27, 28]. In [29], the combination of robust local and global visual feature representations with a time-variant jitter synchronization gives robustness against scale, orientation and affine transforms.…”

Section: Related Workmentioning

confidence: 99%

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Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks

Guzman-Zavaleta

Feregrino-Uribe

2016

PLoS ONE

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Passive content fingerprinting is widely used for video content identification and monitoring. However, many challenges remain unsolved especially for partial-copies detection. The main challenge is to find the right balance between the computational cost of fingerprint extraction and fingerprint dimension, without compromising detection performance against various attacks (robustness). Fast video detection performance is desirable in several modern applications, for instance, in those where video detection involves the use of large video databases or in applications requiring real-time video detection of partial copies, a process whose difficulty increases when videos suffer severe transformations. In this context, conventional fingerprinting methods are not fully suitable to cope with the attacks and transformations mentioned before, either because the robustness of these methods is not enough or because their execution time is very high, where the time bottleneck is commonly found in the fingerprint extraction and matching operations. Motivated by these issues, in this work we propose a content fingerprinting method based on the extraction of a set of independent binary global and local fingerprints. Although these features are robust against common video transformations, their combination is more discriminant against severe video transformations such as signal processing attacks, geometric transformations and temporal and spatial desynchronization. Additionally, we use an efficient multilevel filtering system accelerating the processes of fingerprint extraction and matching. This multilevel filtering system helps to rapidly identify potential similar video copies upon which the fingerprint process is carried out only, thus saving computational time. We tested with datasets of real copied videos, and the results show how our method outperforms state-of-the-art methods regarding detection scores. Furthermore, the granularity of our method makes it suitable for partial-copy detection; that is, by processing only short segments of 1 second length.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks

Guzman-Zavaleta

Feregrino-Uribe

2016

PLoS ONE

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“…Here, we used this small dataset to assess the robustness of features we used to guard against possible video distortions. The ReTRiEVED [77] Dataset was created to evaluate methods that require video quality assessment in transmissions. The ReTRiEVED dataset contains 176 test videos obtained from 8 source videos by applying the transmission parameters listed in Table 16.…”

Section: Retrieved Datasetmentioning

confidence: 99%

“…Since the match numbers differ greatly under different thresholds, we use the 12-step threshold values to find better conditions for SIFT. Figure 12 shows Here, we compared the SIFT features against some related methods: CST−SURF [77], CC [78], and {Th; CC; ORB} [24] for video retrieval. In Table 17, the average detection F1-scores are presented.…”

Section: Attacksmentioning

confidence: 99%

Video Searching and Fingerprint Detection by Using the Image Query and PlaceNet-Based Shot Boundary Detection Method

Jiang

Kim

2018

Applied Sciences

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This work presents a novel shot boundary detection (SBD) method based on the Place-centric deep network (PlaceNet), with the aim of using video shots and image queries for video searching (VS) and fingerprint detection. The SBD method has three stages. In the first stage, we employed Local Binary Pattern-Singular Value Decomposition (LBP-SVD) features for candidate shot boundaries selection. In the second stage, we used the PlaceNet to select the shot boundary by semantic labels. In the third stage, we used the Scale-Invariant Feature Transform (SIFT) descriptor to eliminate falsely detected boundaries. The experimental results show that our SBD method is effective on a series of SBD datasets. In addition, video searching experiments are conducted by using one query image instead of video sequences. The results under several image transitions by using shot fingerprints have shown good precision.

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Robust Temporal Registration Scheme for Video Copies Using Visual-Audio Features

Roopalakshmi

Venkatesh

Rahul

2015

Procedia Computer Science

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A framework for estimating geometric distortions in video copies based on visual-audio fingerprints

Cited by 4 publications

References 11 publications

Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks

Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks

Video Searching and Fingerprint Detection by Using the Image Query and PlaceNet-Based Shot Boundary Detection Method

Robust Temporal Registration Scheme for Video Copies Using Visual-Audio Features

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