Convolutional neural network initialization approaches for image manipulation detection

Camacho, Ivan Castillo; Wang, Kai

doi:10.1016/j.dsp.2021.103376

Cited by 6 publications

(5 citation statements)

References 40 publications

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“…As mentioned above, existing self-supervised learning methods tend to be in favor of typical computer vision tasks related to image content understanding, i.e., semantic classification and object detection. Image forensic tasks are, in general, different from these tasks [4,47,48], because in image forensics we are more interested in forensic traces that can be telltales of the image generation or processing history. These traces are often not related to image semantics and sometimes even not easily visible.…”

Section: Motivationmentioning

confidence: 99%

Self-Supervised Learning for the Distinction between Computer-Graphics Images and Natural Images

Wang

2023

Applied Sciences

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With the increasing visual realism of computer-graphics (CG) images generated by advanced rendering engines, the distinction between CG images and natural images (NIs) has become an important research problem in the image forensics community. Previous research works mainly focused on the conventional supervised learning framework, which usually requires a good quantity of labeled data for training. To our knowledge, we study, for the first time in the literature, the utility of the self-supervised learning mechanism for the forensic classification of CG images and NIs. The idea is to make use of a large number of readily available unlabeled data, along with a self-supervised training procedure on a well-designed pretext task for which labels can be generated in an automatic and convenient way without human manual labeling effort. Differing from existing self-supervised methods, based on pretext tasks targeted at image understanding, or based on contrastive learning, we propose carrying out self-supervised training on a forensics-oriented pretext task of classifying authentic images and their modified versions after applying various manipulations. Experiments and comparisons showed the effectiveness of our method for solving the CG forensics problem under different evaluation scenarios. Our proposed method outperformed existing self-supervised methods in all experiments. It could sometimes achieve comparable, or better, performance. compared with a state-of-the-art fully supervised method under difficult evaluation scenarios with data scarcity and a challenging forensic problem. Our study demonstrates the utility and potential of the self-supervised learning mechanism for image forensics applications.

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Section: Motivationmentioning

confidence: 99%

Self-Supervised Learning for the Distinction between Computer-Graphics Images and Natural Images

Wang

2023

Applied Sciences

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“…Because only one branch participates in coaching, the number of parameters decreases; therefore, the range of layers of the network is additionally reduced, leading to an associated degree of improvement within the speed of forward and backward propagation of the network. Usually, we train 50-100 epochs for the initialization of the network [2,3]. We used the JTA and Microsoft COCO 2014 datasets for joint coaching (Figure 4).…”

Section: Training Experimentsmentioning

confidence: 99%

“…The keys to pose recognition and two-dimensional (2D) animation generation of characters lie in pose extraction, image compression, and the subsequent extraction and application of each action of the character itself. Convolutional neural networks (CNN) have shown great potential for medical image processing [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…However, this method is more challenging and the system complexity is higher. (3) The researcher attempts to generate real-time character animation that conforms to the characteristics of the surrounding environment and character behavior based on the collected 3D posture information, using the surrounding environment and character behavior as input. This method can significantly reduce the workload of animation creators and simplify the complexity of creating high-fidelity virtual character animations.…”

Section: Introductionmentioning

confidence: 99%

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High Speed and Accuracy of Animation 3D Pose Recognition Based on an Improved Deep Convolution Neural Network

Ding

2023

Applied Sciences

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Pose recognition in character animations is an important avenue of research in computer graphics. However, the current use of traditional artificial intelligence algorithms to recognize animation gestures faces hurdles such as low accuracy and speed. Therefore, to overcome the above problems, this paper proposes a real-time 3D pose recognition system, which includes both facial and body poses, based on deep convolutional neural networks and further designs a single-purpose 3D pose estimation system. First, we transformed the human pose extracted from the input image to an abstract pose data structure. Subsequently, we generated the required character animation at runtime based on the transformed dataset. This challenges the conventional concept of monocular 3D pose estimation, which is extremely difficult to achieve. It can also achieve real-time running speed at a resolution of 384 fps. The proposed method was used to identify multiple-character animation using multiple datasets (Microsoft COCO 2014, CMU Panoptic, Human3.6M, and JTA). The results indicated that the improved algorithm improved the recognition accuracy and performance by approximately 3.5% and 8–10 times, respectively, which is significantly superior to other classic algorithms. Furthermore, we tested the proposed system on multiple pose-recognition datasets. The 3D attitude estimation system speed can reach 24 fps with an error of 100 mm, which is considerably less than that of the 2D attitude estimation system with a speed of 60 fps. The pose recognition based on deep learning proposed in this study yielded surprisingly superior performance, proving that the use of deep-learning technology for image recognition has great potential.

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“…Convolutional neural network is a representative algorithm of deep learning. Through weight sharing and local connection, it can reduce not only the complexity of the network model but also the number of weights [8][9][10][11].…”

Section: Related Workmentioning

confidence: 99%

Research on Web GUI Image Recognition System Based on Improved Convolutional Neural Network

Zhao

2022

JWE

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In this paper, a MobileNet V2 convolutional neural network depending on L2 regularization method, a amended particle swarm model and Dropout method are constructed in a bid for enhancing the accuracy and speed of Web GUI image recognition for establishing Web Web GUI image recognition system of Web GUI test. Firstly, an improved MobileNet V2 convolutional neural network is constructed. Secondly, the basic models of L2 regularization method, improved particle swarm method and Dropout method are studied, the improved MobileNet V2 convolution neural network optimization algorithm is proposed, and the basic model of image processing is designed. Finally, simulation analysis is carried out on Web GUI image recognition through using the amended convolutional neural network on basis of MINIST data set as the research object. The simulation results illustrate that the amended convolutional neural network proposed in this study is more accurate and efficient in Web GUI image recognition.

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Convolutional neural network initialization approaches for image manipulation detection

Cited by 6 publications

References 40 publications

Self-Supervised Learning for the Distinction between Computer-Graphics Images and Natural Images

Self-Supervised Learning for the Distinction between Computer-Graphics Images and Natural Images

High Speed and Accuracy of Animation 3D Pose Recognition Based on an Improved Deep Convolution Neural Network

Research on Web GUI Image Recognition System Based on Improved Convolutional Neural Network

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