Robust Detection of Image Operator Chain With Two-Stream Convolutional Neural Network

Liao, Xin; Li, Kaide; Zhu, Xiaoyan; Liu, K. J. Ray

doi:10.1109/jstsp.2020.3002391

Cited by 147 publications

(56 citation statements)

References 35 publications

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“…This is because CNNs can automatically learn the classification features directly from data. It is perceived from the existing literature that attempts have been made towards the implementation of CNNs based general-purpose forensic schemes [32]- [38]. A new convolution network is suggested in [32] based on the concept of suppressing the image content information.…”

Section: Related Workmentioning

confidence: 99%

SDCN2: A Shallow Densely Connected CNN for Multi-Purpose Image Manipulation Detection

Singh

Goyal

2022

ACM Trans. Multimedia Comput. Commun. Appl.

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The digital image information can be easily tampered to harm the integrity of someone. Thus, recognizing the truthfulness and processing history of an image is one of the essential concerns in multimedia forensics. Numerous forensic methods have been developed by researchers with the ability to detect targeted editing operations. But, creating a unified forensic approach capable of detecting multiple image manipulations is still a challenging problem. In this paper, a new general-purpose forensic approach is designed based on a shallow densely connected CNN (SDCN2) that exploits local dense connections and global residual learning. The residual domain is considered in the proposed network rather than the spatial domain to analyze the image manipulation artifacts because the residual domain is less dependent on image content information. To attain this purpose, a residual convolutional layer is employed at the beginning of the proposed model to adaptively learn the image manipulation features by suppressing the image content information. Then, the obtained image residuals or prediction error features are further processed by the shallow densely connected CNN for high-level feature extraction. Also, the hierarchical features produced by the densely connected blocks (DCBs) and prediction error features are fused globally for better information flow across the network. The extensive experiment results show that the proposed scheme outperforms the existing state-of-the-art general-purpose forensic schemes even under anti-forensic attacks, when tested on large scale datasets. The proposed model offers overall detection accuracies of 98.34% and 99.22% for BOSSBase and Dresden datasets, respectively for multiple image manipulation detection. Moreover, the proposed network is highly efficient in terms of computational complexity as compared to the existing approaches.

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Section: Related Workmentioning

confidence: 99%

SDCN2: A Shallow Densely Connected CNN for Multi-Purpose Image Manipulation Detection

Singh

Goyal

2022

ACM Trans. Multimedia Comput. Commun. Appl.

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“…e experimental results show that none of these methods can make full use of motion information and can only make moderate improvements to a single frame. e dual-stream convolutional neural network proposed by Liao et al [18] trains the second CNN stream on the optical flow of the video frame to compensate for the defect that the superimposed RGB stream cannot make full use of time information. Here comes a certain performance gain.…”

Section: Related Knowledgementioning

confidence: 99%

Study of Human Motion Recognition Algorithm Based on Multichannel 3D Convolutional Neural Network

Yang

2021

Complexity

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Aiming at the problem that it is difficult to balance the speed and accuracy of human behaviour recognition, this paper proposes a method of motion recognition based on random projection. Firstly, the optical flow picture and Red, Green, Blue (RGB) picture obtained by the Lucas-Kanade algorithm are used. Secondly, the data of optical flow pictures and RGB pictures are compressed based on a random projection matrix of compressed sensing, which effectively reduces power consumption. At the same time, based on random projection compression data, it can effectively find the optimal linear representation to reconstruct training samples and test samples. Thirdly, a multichannel 3D convolutional neural network is proposed, and the multiple information extracted by the network is fused to form an output recognizer. Experimental results show that the algorithm in this paper significantly improves the recognition rate of multicategory actions and effectively reduces the computational complexity and running time of the recognition algorithm.

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“…Deep learning is a branch of machine learning. With the continuous development of big data and computing power, deep learning methods are rapidly emerging and have been widely used in various fields, such as image detection and speech recognition [20]. At the same time, many studies are applying deep learning to abnormal traffic detection.…”

Section: B Deep Learningmentioning

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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Robust Detection of Image Operator Chain With Two-Stream Convolutional Neural Network

Cited by 147 publications

References 35 publications

SDCN2: A Shallow Densely Connected CNN for Multi-Purpose Image Manipulation Detection

SDCN2: A Shallow Densely Connected CNN for Multi-Purpose Image Manipulation Detection

Study of Human Motion Recognition Algorithm Based on Multichannel 3D Convolutional Neural Network

Efficient BiSRU Combined With Feature Dimensionality Reduction for Abnormal Traffic Detection

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