Exposing computer generated images by using deep convolutional neural networks

Rezende, Edmar; Ruppert, Guilherme C. S.; Theóphilo, Antônio; Tokuda, Eric K.; Carvalho, Tiago

doi:10.1016/j.image.2018.04.006

Cited by 35 publications

(26 citation statements)

References 31 publications

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“…where L is the width or height of the image, K is the kernel size, P is the number of zero paddings, and S is the stride. All the feature maps will go to the next operations which are the convolution layer with identity shortcut and the convolution layer with projection shortcut [32]. The identity shortcut as shown in Figure 10(a) is used when the output feature maps volume is the same as the input feature map volume.…”

Section: = 1 ∑ =1mentioning

confidence: 99%

Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network

2021

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Attentive learning is an important feature of the learning process. It provides a beneficial learning experience and plays a key role in generating positive learning outcomes. Most studies widely applied electroencephalogram (EEG) to measure human attention level. Although most studies use EEG handcrafted features and statistical methods to classify attention level, a more effective feature learning technique is still needed. In this paper, we aim to analyze participants' EEG signals through a deep learning model and classify those signals as showing either attentive or inattentive behaviors. To carry out this research, we initially conducted a background study on attention and its detection in EEG. After that, we design a Troxler's fading experiment and use an EEG device to collect data on participants' attentive and inattentive behaviors during the test. The collected EEG data will be analyzed using a Convolution Attention Memory Neural Network (CAMNN) model to classify participants' attention level. The proposed CAMNN model is optimized with Vector-to-Vector (Vec2Vec) modeling, where the model can be learned through deep neural networks in an end-to-end approach. The result shows that our model can achieve 92% accuracy and 0.92 F1 score which outperforms several existing neural network models such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN), Deep Learning with Convolutional Neural Networks (deep ConvNets), and Compact Convolutional Network for EEG-based BCIs (EEGNet). This research can be useful for those who are interested in developing attention level monitoring or biofeedback system in areas such as educational classroom learning, medical research, and industrial operator.

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Section: = 1 ∑ =1mentioning

confidence: 99%

Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network

2021

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“…Digital content has evolved over a period of time with the advances in the computer graphics, internet, and digital contents. The advances are utilized for many applications in computer vision and image recognition applications [15]. However, the downside of the exploitation of these applications lies in the fact of analysis of creating fake images and videos.…”

Section: Related Workmentioning

confidence: 99%

“…Further, CNN combined with LSTM was used for image tampering detection using the various layers of CNN. Residual-based networks such as ResNet 50 were used in [15] for image tampering detection using the input of computer-generated images.…”

Section: Related Workmentioning

confidence: 99%

Cloud-Based Fusion of Residual Exploitation-Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics

Doegar

Hiriyannaiah

Matt³

et al. 2021

BioMed Research International

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Cloud computing has evolved in various application areas such as medical imaging and bioinformatics. It raises the issues of privacy and tampering in the images especially related to the medical field and bioinformatics for various reasons. The digital images are quite vulnerable to be tampered by the interceptors. The credibility of individuals can transform through falsified information in the images. Image tampering detection is an approach to identifying and finding the tampered components in the image. For the efficient detection of image tampering, the sufficient number of features are required which can be achieved by a deep learning architecture-based models without manual feature extraction of functions. In this research work, we have presented and implemented a cloud-based residual exploitation-based deep learning architectures to detect whether or not an image is being tampered. The proposed approach is implemented on the publicly available benchmark MICC-F220 dataset with the k -fold cross-validation approach to avoid the overfitting problem and to evaluate the performance metrics.

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“…In this section, we briefly introduce the two classical ML methods we used to classify the dataset. The Support Vector Machine (SVM) (Widodo and Yang, 2007) and Convolutional Neural Network (CNN) (Krizhevsky et al, 2012;de Rezende et al, 2018), both are supervised learning methods, meaning that they require a labeled dataset to be trained. In this study, labels correspond to CoT and SPT techniques.…”

Section: Classification With Learning Algorithmsmentioning

confidence: 99%

Applications of Learning Methods to Imaging Issues in Archaeology, Regarding Ancient Ceramic Manufacturing

Dia

Coli

Blanc‐Féraud

et al. 2021

Proceedings of the 2nd International Conference on Deep Learning Theory and Applications

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Archaeological studies involve more and more numerical data analyses. In this work, we are interested in the analysis and classification of ceramic sherds tomographic images in order to help archaeologists in learning about the fabrication processes of ancient pottery. More specifically, a particular manufacturing process (spiral patchwork) has recently been discovered in early Neolithic Mediterranean sites, along with a more traditional coiling technique. It has been shown that the ceramic pore distribution in available tomographic images of both archaeological and experimental samples can reveal which manufacturing technique was used. Indeed, with the spiral patchwork, the pores exhibit spiral-like behaviours, whereas with the traditional one, they are distributed along parallel lines, especially in the experimental samples. However, in archaeological samples, these distributions are very noisy, making analysis and discrimination hard to process. Here, we investigate how Learning Methods (Deep Learning and Support Vector Machine) can be used to answer these numerically difficult problems. In particular, we study how the results depend on the input data (either raw data at the output of the tomographic device, or after a preliminary pore segmentation step), and the quality of the information they could provide to archaeologists.

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Exposing computer generated images by using deep convolutional neural networks

Cited by 35 publications

References 31 publications

Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network

Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network

Cloud-Based Fusion of Residual Exploitation-Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics

Applications of Learning Methods to Imaging Issues in Archaeology, Regarding Ancient Ceramic Manufacturing

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