Signature Verification Using Siamese Convolutional Neural Networks

This research entails the processes undergone in building a Siamese Neural Network for Signature Verification. This Neural Network which uses two similar base neural networks as its underlying architecture was built, trained and evaluated in this project. The base networks were made up of two similar convolutional neural networks sharing the same weights during training. The architecture commonly known as the Siamese network helped reduce the amount of training data needed for its implementation and thus increased the model’s efficiency by 13%. The convolutional network was made up of three convolutional layers, three pooling layers and one fully connected layer onto which the final results were passed to the contrastive loss function for comparison. A threshold function determined if the signatures were forged or not. An accuracy of 78% initially achieved led to the tweaking and improvement of the model to achieve a better prediction accuracy of 93%

Pre-IdentifyNet: An Improved Neural Network for Image Recognition

With the rise and development of artificial intelligence, image recognition and classification technology has received more and more attention as an important branch of its research field. Among them, the introduction of deep learning networks and the construction of neural network structures not only avoid a lot of the tedious work of manual extraction, but also improve the accuracy of image recognition. Convolutional neural networks have many advantages that conventional neural networks do not have. Therefore, image classification systems based on convolutional neural networks emerge in endlessly, but there is still much room for improvement in terms of recognition accuracy and recognition speed. Based on this, this paper proposes an improved deep convolutional neural network to improve the accuracy of the network by changing a series of parameters such as the number of channels of the convolution layer, the size of the convolution kernel, the learning rate, the number of iterations, and the size of the small batch with speed. In this paper, three data sets were selected, namely sewage, animals and the Simpson Family. Comparing the improved convolutional neural network network with the existing SqueezeNet and GoogleNet. It is found that the accuracy of the network is maintained while maintaining a similar speed. Both F1-score and F1-score have been improved with a higher recognition rate and better recognition effect in image recognition classification

Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning. In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN). Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector

Risk of Bias Assessment in Preclinical Literature using Natural Language Processing

We sought to apply natural language processing to the task of automatic risk of bias assessment in preclinical literature, which could speed the process of systematic review, provide information to guide research improvement activity, and support translation from preclinical to clinical research. We use 7840 full‐text publications describing animal experiments with yes/no annotations for five risk of bias items. We implement a series of models including baselines (support vector machine, logistic regression, random forest), neural models (convolutional neural network, recurrent neural network with attention, hierarchical neural network) and models using BERT with two strategies (document chunk pooling and sentence extraction). We tune hyperparameters to obtain the highest F1 scores for each risk of bias item on the validation set and compare evaluation results on the test set to our previous regular expression approach. The F1 scores of best models on test set are 82.0% for random allocation, 81.6% for blinded assessment of outcome, 82.6% for conflict of interests, 91.4% for compliance with animal welfare regulations and 46.6% for reporting animals excluded from analysis. Our models significantly outperform regular expressions for four risk of bias items. For random allocation, blinded assessment of outcome, conflict of interests and animal exclusions, neural models achieve good performance; for animal welfare regulations, BERT model with a sentence extraction strategy works better. Convolutional neural networks are the overall best models. The tool is publicly available which may contribute to the future monitoring of risk of bias reporting for research improvement activities

ANALYSIS OF MULTIPLE ADVERSARIAL ATTACKS ON CONVOLUTIONAL NEURAL NETWORKS

The thesis studies different kind of adversarial attacks on Convolutional Neural Network by using electric load data set in order to fool deep neural network. With the improvement of Deep Learning methods, their securities and vulnerabilities have become an important research subject. An adversary who gains access to the model and data sets may add some perturbations to the datasets, which may cause significant damage to the system. By using adversarial attacks, it shows how much these attacks affect the system and shows the attacks\u27 success in this research

A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition

Convolutional neural networks are sensitive to unknown noisy condition in the test phase and so their performance degrades for the noisy data classification task including noisy speech recognition. In this research, a new convolutional neural network (CNN) model with data uncertainty handling; referred as NCNN (Neutrosophic Convolutional Neural Network); is proposed for classification task. Here, speech signals are used as input data and their noise is modeled as uncertainty. In this task, using speech spectrogram, a definition of uncertainty is proposed in neutrosophic (NS) domain. Uncertainty is computed for each Time-frequency point of speech spectrogram as like a pixel. Therefore, uncertainty matrix with the same size of spectrogram is created in NS domain. In the next step, a two parallel paths CNN classification model is proposed. Speech spectrogram is used as input of the first path and uncertainty matrix for the second path. The outputs of two paths are combined to compute the final output of the classifier. To show the effectiveness of the proposed method, it has been compared with conventional CNN on the isolated words of Aurora2 dataset. The proposed method achieves the average accuracy of 85.96 in noisy train data. It is more robust against Car, Airport and Subway noises with accuracies 90, 88 and 81 in test sets A, B and C, respectively. Results show that the proposed method outperforms conventional CNN with the improvement of 6, 5 and 2 percentage in test set A, test set B and test sets C, respectively. It means that the proposed method is more robust against noisy data and handle these data effectively.Comment: International conference on Pattern Recognition and Image Analysis (IPRIA 2019

Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks

Learning deeper convolutional neural networks becomes a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be gained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, that encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015 Scene Classification Challenge. Extensive experiments on two challenging large scale datasets demonstrate the effectiveness of our method is not restricted to a specific dataset or network architecture. Our models will be available to the research community later.Comment: Technical report for our submissions to the ILSVRC 2015 Scene Classification Challenge, where we won the first plac