A Robust and Fast Video Copy Detection System Using Content-Based Fingerprinting

Esmaeili, Mani Malek; Fatourechi, Mehrdad; Ward, Rabab K.

doi:10.1109/tifs.2010.2097593

Cited by 142 publications

(135 citation statements)

References 18 publications

Supporting

Mentioning

135

Contrasting

Order By: Relevance

“…One of the straightforward pre-processing techniques to reduce the effect of temporal distortions consists of re-sampling the video at a small frame rate [10][12] [20]. In the proposed system, the video is re-sampled at 5 frames per second.…”

Section: A Pre-processingmentioning

confidence: 99%

“…Output normalized sequence using (20). original signal has undergone a circular shift distortion; both of these signals yield the same normalized sequence as shown in Fig.…”

Section: ) Dct/dst-based Signal Normalizationmentioning

confidence: 99%

“…Xu et al have proposed a video copy detection scheme where the selected low and middle frequency DCT-coefficients of each key-frame are used as a signature [19]. More recently, Esmaeili et al have formed temporally informative representative images from the video sequence, referred to as TIRIs, in order to extract a binary fingerprint from the features that can be obtained in the DCT domain of the overlapping blocks of the TIRI frames [20]. In [21], an idea for generating weights for a given hash based on visual saliency has been proposed for efficient matching.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Perceptual Video Hashing for Content Identification and Authentication

Khelifi

Bouridane

2019

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

Abstract-Perceptual hashing has been broadly used in the literature to identify similar contents for video copy detection. It has also been adopted to detect malicious manipulations for video authentication. However, targeting both applications with a single system using the same hash would be highly desirable as this saves the storage space and reduces the computational complexity. This paper proposes a perceptual video hashing system for content identification and authentication. The objective is to design a hash extraction technique that can withstand signal processing operations on one hand and detect malicious attacks on the other hand. The proposed system relies on a new signal calibration technique for extracting the hash using the discrete cosine transform (DCT) and the discrete sine transform (DST). This consists of determining the number of samples, called the normalizing shift, that is required for shifting a digital signal so that the shifted version matches a certain pattern according to DCT/DST coefficients. The rationale for the calibration idea is that the normalizing shift resists signal processing operations while it exhibits sensitivity to local tampering (i.e., replacing a small portion of the signal with a different one). While the same hash serves both applications, two different similarity measures have been proposed for video identification and authentication, respectively. Through intensive experiments with various types of video distortions and manipulations, the proposed system has been shown to outperform related state-of-the art video hashing techniques in terms of identification and authentication with the advantageous ability to locate tampered regions.

show abstract

Section: A Pre-processingmentioning

confidence: 99%

“…Output normalized sequence using (20). original signal has undergone a circular shift distortion; both of these signals yield the same normalized sequence as shown in Fig.…”

Section: ) Dct/dst-based Signal Normalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Perceptual Video Hashing for Content Identification and Authentication

Khelifi

Bouridane

2019

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

show abstract

“…Moreover, for judging the image copy relationship, the matching of these features is also a time-consuming process. As a result, the computational problem makes them less appealing in copy detection [26].…”

Section: Copyright Cmentioning

confidence: 99%

Effective and Efficient Image Copy Detection with Resistance to Arbitrary Rotation

Zhou

Yang

Chen

et al. 2016

IEICE Trans. Inf. & Syst.

109

View full text Add to dashboard Cite

SUMMARY For detecting the image copies of a given original image generated by arbitrary rotation, the existing image copy detection methods can not simultaneously achieve desirable performances in the aspects of both accuracy and efficiency. To address this challenge, a novel effective and efficient image copy detection method is proposed based on two global features extracted from rotation invariant partitions. Firstly, candidate images are preprocessed by an averaging operation to suppress noise. Secondly, the rotation invariant partitions of the preprocessed images are constructed based on pixel intensity orders. Thirdly, two global features are extracted from these partitions by utilizing image gradient magnitudes and orientations, respectively. Finally, the extracted features of images are compared to implement copy detection. Promising experimental results demonstrate our proposed method can effectively and efficiently resist rotations with arbitrary degrees. Furthermore, the performances of the proposed method are also desirable for resisting other typical copy attacks, such as flipping, rescaling, illumination and contrast change, as well as Gaussian noising.

show abstract

“…[10]. Besides video signatures, fingerprint [15] or index codes [18] were also researched for fast matching.…”

Section: Related Workmentioning

confidence: 99%

Efficient Large-Scale Video Retrieval via Discriminative Signatures

Hao

Kamata

2013

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Pengyi HAO †a) , Student Member and Sei-ichiro KAMATA †b) , Member SUMMARYThe topic of retrieving videos containing a desired person from a dataset just using the content of faces without any help of textual information has many interesting applications like video surveillance, social network, video mining, etc. However, traditional face matching against a huge number of detected faces leads to an unacceptable response time and may also reduce the accuracy due to the large variations in facial expressions, poses, lighting, etc. Therefore, in this paper we propose a novel method to generate discriminative "signatures" for efficiently retrieving the videos containing the same person with a query. In this research, the signature is defined as a compact, discriminative and reduced dimensionality representation, which is generated from a set of high-dimensional feature vectors of an individual. The desired videos are retrieved based on the similarities between the signature of the query and those of individuals in the database. In particular, we make the following contributions. Firstly, we give an algorithm of two directional linear discriminant analysis with maximum correntropy criterion (2DLDA-MCC) as an extension to our recently proposed maximum correntropy criterion based linear discriminant analysis (LDA-MCC). Both algorithms are robust to outliers and noise. Secondly, we present an approach for transferring a set of exemplars to a fixed-length signature using LDA-MCC and 2DLDA-MCC, resulting in two kinds of signatures that are called 1D signature and 2D signature. Finally, a novel video retrieval scheme is given based on the signatures, which has low storage requirement and can achieve a fast search. Evaluations on a large dataset of videos show reliable measurement of similarities by using the proposed signatures to represent the identities generated from videos. Experimental results also demonstrate that the proposed video retrieval scheme has the potential to substantially reduce the response time and slightly increase the mean average precision of retrieval. key words : individual retrieval, signature, linear discriminant analysis, video dataset

show abstract

A Robust and Fast Video Copy Detection System Using Content-Based Fingerprinting

Cited by 142 publications

References 18 publications

Perceptual Video Hashing for Content Identification and Authentication

Perceptual Video Hashing for Content Identification and Authentication

Effective and Efficient Image Copy Detection with Resistance to Arbitrary Rotation

Efficient Large-Scale Video Retrieval via Discriminative Signatures

Contact Info

Product

Resources

About