Improving Diversity and Quality of Adversarial Examples in Adversarial Transformation Network

This paper proposes a method to mitigate two major issues of Adversarial Transformation Networks (ATN) including the low diversity and the low quality of adversarial examples. In order to deal with the first issue, this research proposes a stacked convolutional autoencoder based on pattern to generalize ATN. This proposed autoencoder could support different patterns such as all-feature pattern , border feature pattern , and class model map pattern . In order to deal with the second issue, this paper presents an algorithm to improve the quality of adversarial examples in terms of L 0 -norm and L 2 -norm. This algorithm employs an adversarial feature ranking heuristics such as JSMA and COI to prioritize adversarial features. To demonstrate the advantages of the proposed method, comprehensive experiments have been conducted on the MNIST dataset and the CIFAR-10 dataset. For the first issue, the proposed autoencoder can generate diverse adversarial examples with the average success rate above 99%. For the second issue, the proposed algorithm could not only improve the quality of adversarial examples significantly but also maintain the average success rate. In terms of L 0 -norm, the proposed algorithm could decrease from hundreds of adversarial features to one adversarial feature. In terms of L 2 -norm, the proposed algorithm could reduce the average distance considerably. These results show that the proposed method is capable of generating high-quality and diverse adversarial examples in practice.

Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization

Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital transformation is structured, which makes it difficult to utilize the advantages of CNN. This paper introduces a novel autoencoder, named Half Convolutional Autoencoder, which adopts convolutional layers to discover the high-order correlation between structured features in the form of Tag Genome, the side information associated with each movie in the MovieLens 20 M dataset, in order to generate a robust feature vector. Subsequently, these new movie representations, along with the introduction of users’ characteristics generated via Tag Genome and their past transactions, are applied into well-known matrix factorization models to resolve the initialization problem and enhance the predicting results. This method not only outperforms traditional matrix factorization techniques by at least 5.35% in terms of accuracy but also stabilizes the training process and guarantees faster convergence.

Automatic Diagnosis of Epileptic Seizures in EEG Signals Using Fractal Dimension Features and Convolutional Autoencoder Method

This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.