Video Object Forgery Detection Algorithm Based on VGG-11 Convolutional Neural Network

Gan, Yanfen; Yang, Jixiang; Lai, Wenda

doi:10.1109/icicas48597.2019.00126

Cited by 16 publications

(7 citation statements)

References 11 publications

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“…The first set is composed of three less dense neural network architectures, and the second set is composed of three dense-layer neural networks. For the less dense models, we took VGG-11 [ 27 , 28 ], VGG-16 [ 27 ] and MobileNetV2 [ 29 ]. For the second set, for the dense-layer models, we choose ResNet50 [ 30 , 31 ], ResNet101 [ 30 ] and Darknet53 [ 32 ].…”

Section: Experiments and Analysismentioning

confidence: 99%

Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning Auditing Applications

Pathmakumar

Elara

Soundararajan

et al. 2022

Sensors

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Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the cleanliness delivered by the various cleaning methods remain manual and often ignored. This work presents a novel domestic dirt image dataset for cleaning auditing application including AI-based dirt analysis and robot-assisted cleaning inspection. One of the significant challenges in an AI-based robot-aided cleaning auditing is the absence of a comprehensive dataset for dirt analysis. We bridge this gap by identifying nine classes of commonly occurring domestic dirt and a labeled dataset consisting of 3000 microscope dirt images curated from a semi-indoor environment. The dirt dataset gathered using the adhesive dirt lifting method can enhance the current dirt sensing and dirt composition estimation for cleaning auditing. The dataset’s quality is analyzed by AI-based dirt analysis and a robot-aided cleaning auditing task using six standard classification models. The models trained with the dirt dataset were capable of yielding a classification accuracy above 90% in the offline dirt analysis experiment and 82% in real-time test results.

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Section: Experiments and Analysismentioning

confidence: 99%

Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning Auditing Applications

Pathmakumar

Elara

Soundararajan

et al. 2022

Sensors

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“…However, the performance of this model reduces when a video is compressed with a low bit rate (heavily compressed videos). Another scheme proposed by Gan et al (2019) exploits "VGG-11" (Simonyan and Zisserman 2015) Deep Learning model to detect forged videos/frames. At first, motion residuals and steganalysis features are extracted as reported in Chen et al (2016).…”

Section: Object-based Video Forgery Detectionmentioning

confidence: 99%

“…(c) Forged frames: Manipulated frames, which have undergone the recompression process to generate a forged video. To detect the above forgery in a video, Chen et al (2016) and Gan et al (2019) extract some handcrafted steganalysis features (Kodovskỳ and Fridrich 2009;Kodovsky et al 2011;Kodovsky and Fridrich 2012) and those features are fed into an ensemble classifier (Kodovsky et al 2011) andVGG-11 network (Simonyan andZisserman 2015), respectively.…”

Section: Introductionmentioning

confidence: 99%

Object-based forgery detection in surveillance video using capsule network

Bakas

Naskar

Nappi

et al. 2021

J Ambient Intell Human Comput

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Due to the rapid growth of digital multimedia technology, the importance of multimedia data such as digital images/videos is increasing rapidly in diverse domains. This includes surveillance CCTV footages, which act as primary evidences in many court cases, including highly sensitive contexts. Today, with the wide availability of low-cost image/video manipulating software, digital images/videos have become highly vulnerable to manipulation/modification attacks; one such attack is the class of object-based forgery in surveillance videos. In this paper, we propose a Capsule Network based digital forensic technique for detection of object-based forgery in surveillance videos. In the proposed technique, we use motion residual, computed from every video frame, to extract intra-and inter-frame inherent statistical characteristics of the video sequence, as the input of capsule network. Our experimental results indicate that the proposed technique achieves significant performance in terms of authentic, double compressed and forged frame detection, irrespective of the group of pictures length and degree of compression in videos.

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“…Much of the current progress in deep learning has come from increased modelling power and associated computation, which often involves larger, deeper neural networks [34]- [36]. However, although the deep neural network brings obvious improvement, it also takes a great deal of training time.…”

Section: B Simple Recurrent Unitmentioning

confidence: 99%

Efficient BiSRU Combined With Feature Dimensionality Reduction for Abnormal Traffic Detection

Ding

Wen

et al. 2020

IEEE Access

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Abnormal traffic detection is an important network security technology to protect computer systems from malicious attacks. Existing detection methods are usually based on traditional machine learning, such as Support Vector Machine (SVM), Naive Bayes, etc. They rely heavily on manual design of traffic features and usually shallow feature learning, which get a low accuracy for high-dimensional traffic. Although the method based on Long Short-Term Memory (LSTM) has an excellent ability to detect abnormal traffic. The sequence-dependent structure of LSTM cannot realize parallel computation, which leads to slow model training and limits its applicability. To address the above problem, we propose an efficient Bidirectional Simple Recurrent Unit (BiSRU) combined with feature dimensionality reduction for abnormal traffic detection. Specifically, in order to perform feature dimensionality reduction on the original high-dimensional network traffic, we design a stack Sparse Autoencoder (sSAE) to extract the compressed high-level features. For the purpose of realizing efficient parallel computation and accurate feature extraction, a BiSRU is utilized to extract the bidirectional structural features of the traffic. Finally, the experimental results show that our proposed method significantly outperforms existing methods in terms of accuracy and training time. The method we propose can timely and accurately detect various abnormal traffic and achieve effective network security protection.

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Video Object Forgery Detection Algorithm Based on VGG-11 Convolutional Neural Network

Cited by 16 publications

References 11 publications

Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning Auditing Applications

Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning Auditing Applications

Object-based forgery detection in surveillance video using capsule network

Efficient BiSRU Combined With Feature Dimensionality Reduction for Abnormal Traffic Detection

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