Frame duplication detection based on BoW model

Ulutaş, Güzin; Üstübi̇oğlu, Beste; Ulutaş, Mustafa; Nabiyev, Vasif V.

doi:10.1007/s00530-017-0581-6

Cited by 27 publications

(26 citation statements)

References 24 publications

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“…Execution Time (s/frame) Wang et al [2] 294.67 Lin et al [13] 140.6 Yang et al [17] 0.38 Singh et al [23] 0.024 Ulutas et al [24] 0.01 Ulutas et al [25] 0.2 Proposed Method 0.021…”

Section: Methodsmentioning

confidence: 99%

“…We also rerun all the methods for 100 times to obtain reported average running time. All the background processes were also stopped during execution of the proposed method and the others [2], [13], [17], [23]- [25]. Table 4.…”

Section: Testmentioning

confidence: 99%

“…Similar works in the literature [2], [13], [17], [23], [24], [25] are realized to make a fair comparison with the proposed method. Experimental results show that the proposed method gives better Detection Accuracy, Precision Rate and Recall Rate compared to similar works [2], [13], [17], [23]- [25]. When execution time is considered, the method determines the forged sequences faster than the others [2], [13], [17], [23], [24,[25].…”

Section: Introductionmentioning

confidence: 99%

“…The method extracts binary features from frames and determines the similarity among features. The same authors also proposed another study based on BoW model to detect frame duplication forgery in 2018 [25]. Their method uses BoW to create visual words and build a dictionary from Scale Independent Feature Transform (SIFT) keypoints of frames in video.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Video Forgery Detection Method based on Local Difference Binary

Ulutaş¹,

Üstübi̇oğlu²,

Ulutaş³

et al. 2020

Pamukkale J Eng Sci

Self Cite

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Öz Recently, the rapid development of video editing software has made video forgery applicable. Researchers have proposed methods to detect forged video frames. These methods utilize codec properties, motion artifacts, noise effect and frame similarity to detect forgery. Execution time and low detection accuracy are the two main drawbacks of forgery detection methods reported in the literature. In this study, a new frame duplication detection method using Local Difference Binary (LDB) is proposed to extract features from the frames. Distance between similar frames that have similar feature vectors are is used by the method to estimate Distance of Forgery and to determine the exact location of duplicated frames. PSNR between similar frames are is then used to group them into three classes, and rule-based mechanism reports forged frames according to the membership to classes. Experimental results indicate that the proposed method has lower execution time with higher accuracy than similar works. Son yıllarda, video düzenleme yazılımının hızlı gelişimi video sahteciliğini uygulanabilir hale getirmiştir. Araştırmacılar sahte video karelerini tespit etmek için yöntemler önermiştir. Bu yöntemler sahteciliği tespit etmek için kodek özelliklerini, hareket artefaktlarını, gürültü etkisini ve çerçeve benzerliğini kullanmaktadır. Literatürde sahtecilik tespiti için önerilen yöntemlerin iki ana dezavantajı çalışma zamanı ve düşük tespit doğruluğudur. Bu çalışmada, çerçevelerden özellikler çıkarmak için Yerel Fark İkililerini (LDB) kullanan yeni bir çerçeve tekrarlama tespit yöntemi önerilmiştir. Benzer özellik vektörlerine sahip benzer çerçeveler arasındaki mesafe, Sahtecilik Mesafesini tahmin etmek ve kopyalanan çerçevelerin tam yerini belirlemek için kullanılmaktadır. Benzer çerçeveler arasındaki PSNR daha sonra bunları üç sınıfa gruplamak için kullanılır ve kural tabanlı mekanizma sahte çerçeveleri sınıf üyeliklerine göre raporlar. Deneysel sonuçlar, önerilen yöntemin, benzer çalışmalara kıyasla daha yüksek hassasiyetle daha düşük uygulama süresine sahip olduğunu göstermektedir.

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“…Execution Time (s/frame) Wang et al [2] 294.67 Lin et al [13] 140.6 Yang et al [17] 0.38 Singh et al [23] 0.024 Ulutas et al [24] 0.01 Ulutas et al [25] 0.2 Proposed Method 0.021…”

Section: Methodsmentioning

confidence: 99%

Section: Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Video Forgery Detection Method based on Local Difference Binary

Ulutaş¹,

Üstübi̇oğlu²,

Ulutaş³

et al. 2020

Pamukkale J Eng Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bag-of-words (BoW for short) is one of traditional visual representation methods for image and video representation, which is proposed by Sivic and Zisserman (2003) who used this model to index videos throughout a movie database. Ulutas et al (2018) presented a novel approach to solve the problem of frame duplication detection in a video. They utilized BoW model to generate visual words and build a dictionary from Scale Independent Feature Transform (SIFT for short) keypoints of frames in video.…”

Section: Related Workmentioning

confidence: 99%

CNN-VWII: An efficient approach for large-scale video retrieval by image queries

Zhang

Lin

Zhu

et al. 2019

Pattern Recognition Letters

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This paper aims to solve the problem of large-scale video retrieval by a query image. Firstly, we define the problem of top-k image to video query. Then, we combine the merits of convolutional neural networks(CNN for short) and Bag of Visual Word(BoVW for short) module to design a model for video frames information extraction and representation. In order to meet the requirements of large-scale video retrieval, we proposed a visual weighted inverted index(VWII for short) and related algorithm to improve the efficiency and accuracy of retrieval process. Comprehensive experiments show that our proposed technique achieves substantial improvements (up to an order of magnitude speed up) over the state-of-the-art techniques with similar accuracy.

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Surveillance Video Authentication Using Universal Image Quality Index of Temporal Average

Fadl

Han

2019

Digital Forensics and Watermarking

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Frame duplication detection based on BoW model

Cited by 27 publications

References 24 publications

Video Forgery Detection Method based on Local Difference Binary

Video Forgery Detection Method based on Local Difference Binary

CNN-VWII: An efficient approach for large-scale video retrieval by image queries

Surveillance Video Authentication Using Universal Image Quality Index of Temporal Average

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