Deep Adversarial Metric Learning.

Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG. Furthermore, recent methods have mostly trained and evaluated based on single session EEG data. We address this problem from an invariant representation learning perspective. We propose an adversarial inference approach to extend such deep learning models to learn session-invariant person-discriminative representations that can provide robustness in terms of longitudinal usability. Using adversarial learning within a deep convolutional network, we empirically assess and show improvements with our approach based on longitudinally collected EEG data for person identification from half-second EEG epochs.

Path-based multi-hop reasoning over knowledge graph for answering questions via adversarial reinforcement learning

This book provided an overview of the field of adversarial machine learning. Important issues were clearly left out, some deliberately to simplify exposition, others perhaps unintentionally. The field has become quite active in recent years in no small measure due to the attention that attacks on deep learning methods have received. While we devote an entire chapter solely to adversarial deep learning, we emphasize that proper understanding of these necessitates a broader look at adversarial learning that the rest of the book provides.

Graph contrastive learning has emerged as a powerful unsupervised graph representation learning tool. The key to the success of graph contrastive learning is to acquire high-quality positive and negative samples as contrasting pairs to learn the underlying structural semantics of the input graph. Recent works usually sample negative samples from the same training batch with the positive samples or from an external irrelevant graph. However, a significant limitation lies in such strategies: the unavoidable problem of sampling false negative samples. In this paper, we propose a novel method to utilize Counterfactual mechanism to generate artificial hard negative samples for Graph Contrastive learning, namely CGC. We utilize a counterfactual mechanism to produce hard negative samples, ensuring that the generated samples are similar but have labels that differ from the positive sample. The proposed method achieves satisfying results on several datasets. It outperforms some traditional unsupervised graph learning methods and some SOTA graph contrastive learning methods. We also conducted some supplementary experiments to illustrate the proposed method, including the performances of CGC with different hard negative samples and evaluations for hard negative samples generated with different similarity measurements. The implementation code is available online to ease reproducibility1.

Despite the great success, existing regression clustering methods based on shallow models are vulnerable due to: (1) They often pay no attention to the combination between learning representations and clustering, thus resulting in unsatisfactory clustering performance. (2) They ignore the relationship of data distribution and target distribution such that those methods are noise and illumination-change sensitive. (3) These nonlinear regression methods usually impose the hard constraint to minimize the mismatch between the discrete cluster assignment matrix and latent representations, which leads to over-fitting. In this paper, we utilize deep adversarial regression to tackle these problems and formulate regression based clustering by deep adversarial learning (RCDA). By seamlessly combining with the stacked autoencoder, the proposed model integrates learning deep nonlinear latent representation and clustering in a unified framework. Specifically, RCDA uses a kind of relax constraint between latent representations and continuous cluster assignment matrix to avoid over-fitting, and simultaneously utilizes the t-SNE algorithm and adversarial learning to analyze data distribution and target distribution so that improve representations learning. Experimental results on public benchmark datasets demonstrate that the proposed architecture achieves better performance than state-of-the-art clustering models in image clustering task.

Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success in performing various wireless communication tasks such as signal recognition, spectrum sensing and waveform design. However, ML in general and DL in particular have been found vulnerable to manipulations thus giving rise to a field of study called adversarial machine learning (AML). Although AML has been extensively studied in other data domains such as computer vision and natural language processing, research for AML in the wireless communications domain is still in its early stage. This paper presents a comprehensive review of the latest research efforts focused on AML in wireless communications while accounting for the unique characteristics of wireless systems. First, the background of AML attacks on deep neural networks is discussed and a taxonomy of AML attack types is provided. Various methods of generating adversarial examples and attack mechanisms are also described. In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented. Finally, as new attacks and defense techniques are developed, recent research trends and the overarching future outlook for AML in next-generation wireless communications are discussed.

The design of a new therapeutic agent is a time-consuming and expensive process. The rise of machine intelligence provides a grand opportunity of expeditiously discovering novel drug candidates through smart search in the vast molecular structural space. In this paper, we propose a new approach called adversarial deep evolutionary learning (ADEL) to search for novel molecules in the latent space of an adversarial generative model and keep improving the latent representation space. In ADEL, a custom-made adversarial autoencoder (AAE) model is developed and trained under a deep evolutionary learning (DEL) process. This involves an initial training of the AAE model, followed by an integration of multi-objective evolutionary optimization in the continuous latent representation space of the AAE rather than the discrete structural space of molecules. By using the AAE, an arbitrary distribution can be provided to the training of AAE such that the latent representation space is set to that distribution. This allows for a starting latent space from which new samples can be produced. Throughout the process of learning, new samples of high-quality are generated after each iteration of training and then added back into the full dataset. Therefore, allowing for a more comprehensive procedure of understanding the data structure. This combination of evolving data and continuous learning not only enables improvement in the generative model, but the data as well. By comparing ADEL to the previous work in DEL, we see that ADEL can obtain better property distributions.

DCFGAN: An adversarial deep reinforcement learning framework with improved negative sampling for session-based recommender systems

The presence of defects in substation equipment is a major factor affecting the safety of power transmission. Therefore, timely and accurate detection of these defects is crucial. As intelligent inspection robots advance, using mainstream object detection models to diagnose surface defects in substation equipment has become a focal point of current research. However, the lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. To address the issue of insufficient training data for defect images with complex backgrounds, such as rust and surface oil leakage in substation equipment, which leads to the poor performance of detection models, this paper proposes a novel adversarial deep learning model for substation defect image generation: the Abnormal Defect Detection Generative Adversarial Network (ADD-GAN). Unlike existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model uses a joint discriminator for both overall images and defect images to address the issue of low attention to local defect areas, thereby reducing the loss of image features. This approach enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves a mean average precision (mAP) of 81.5% on the test dataset, and outperforms other image data augmentation and generation methods. This confirms that the ADD-GAN method can generate a high-fidelity image dataset of substation equipment defects.

The generative adversarial imitation learning (GAIL) has provided an adversarial learning framework for imitating expert policy from demonstrations in high-dimensional continuous tasks. However, almost all GAIL and its extensions only design a kind of reward function of logarithmic form in the adversarial training strategy with the Jensen-Shannon (JS) divergence for all complex environments. The fixed logarithmic type of reward function may be difficult to solve all complex tasks, and the vanishing gradients problem caused by the JS divergence will harm the adversarial learning process. In this paper, we propose a new algorithm named Wasserstein Distance guided Adversarial Imitation Learning (WDAIL) for promoting the performance of imitation learning (IL). There are three improvements in our method: (a) introducing the Wasserstein distance to obtain more appropriate measure in the adversarial training process, (b) using proximal policy optimization (PPO) in the reinforcement learning stage which is much simpler to implement and makes the algorithm more efficient, and (c) exploring different reward function shapes to suit different tasks for improving the performance. The experiment results show that the learning procedure remains remarkably stable, and achieves significant performance in the complex continuous control tasks of MuJoCo.

Adversarial deep reinforcement learning based robust depth tracking control for underactuated autonomous underwater vehicle

Mobile user profiles are a summary of characteristics of user-specific mobile activities. Mobile user profiling is to extract a user's interest and behavioral patterns from mobile behavioral data. While some efforts have been made for mobile user profiling, existing methods can be improved via representation learning with awareness of substructures in users' behavioral graphs. Specifically, in this paper, we study the problem of mobile users profiling with POI check-in data. To this end, we first construct a graph, where a vertex is a POI category and an edge is the transition frequency of a user between two POI categories, to represent each user. We then formulate mobile user profiling as a task of representation learning from user behavioral graphs. We later develop a deep adversarial substructured learning framework for the task. This framework has two mutually-enhanced components. The first component is to preserve the structure of the entire graph, which is formulated as an encoding-decoding paradigm. In particular, the structure of the entire graph is preserved by minimizing reconstruction loss between an original graph and a reconstructed graph. The second component is to preserve the structure of subgraphs, which is formulated as a substructure detector based adversarial training paradigm. In particular, this paradigm includes a substructure detector and an adversarial trainer. Instead of using non-differentiable substructure detection algorithms, we pre-train a differentiable convolutional neural network as the detector to approximate these detection algorithms. The adversarial trainer is to match the detected substructure of the reconstructed graph to the detected substructure of the original graph. Also, we provide an effective solution for the optimization problems. Moreover, we exploit the learned representations of users for the next activity type prediction. Finally, we present extensive experimental results to demonstrate the improved performances of the proposed method.

Conversational agents are getting more popular and applied in a wide range of practical application areas. The main task of these agents is not only to generate context-appropriate responses to a given query but also to make the conversation human-like. Thanks to the ability of deep learning based models in natural language modeling, recent studies have made progress in designing conversational agents that can provide more semantically accurate responses. However, the naturalness in such conversation setting has not been given adequate attention in these studies. This paper aims to incorporate both important criteria of accuracy and naturalness of conversation in developing a new model for conversational agents. To this end, inspired by the idea of Turing test and the idea of adversarial learning strategy, we propose to design a model based on generative deep neural networks that interestingly allow to generate accurate responses optimized by the mechanics of imitating human-generated conversations. Experimental results demonstrate that the proposed models produce more natural and accurate responses, yielding significant gains in BLEU scores.

Data-driven deep learning tasks for security related applications are gaining increasing popularity and achieving impressive performances. This thesis investigates adversarial vulnerabilities of such tasks in order to establish secure and reliable machine learning systems. Adversary attacks aim to extract private data from a model of a task and misguide the model so it yields wrong results or an answer desired by the attacker. This thesis studies potential adversarial attacks that may affect an existing deep learning model of a specific task. Novel approaches that expose security vulnerabilities of four typical deep learning models in three dominant tasks (i.e., matching, classification and regression) are developed. These models include image hashing for image authentication and retrieval, fake face imagery forensic detection, image classification and single object tracking. In the first model, image hashing converts images into codes that are supposed to be non-invertible. However, we prove that this can pose image privacy concerns, and propose two deep learning de-hashing neural networks to show that we can obtain high quality images that are inverted from given image hashes. In the second model, we address fake face image detection. Fake images that can escape an adversarial attacked detector are usually degraded versions of original images. We analyze the visual degradation in such face images, and show how to design attacks that result in visually imperceptible adversarial images. For the image classification model, instead of the conventionally employed visual distortion metric, we propose the use of perceptual models as a novel measure for adversarial example generation. We then propose two sets of attack methods that can generally be incorporated into all existing gradient-based attacks. Lastly, for the single object tracking model, we propose the concept of universally and physically feasible attacks on visual object tracking in real-world settings. We develop a novel attack framework and experimentally demonstrate the feasibility of the proposed concept. The adversarial explorations and examples provided in this thesis show how existing deep learning tasks and their models could be vulnerable to malicious attacks. This would help researchers design more secure and trustworthy models for digital media security and forensics.

Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and personal experiences. To alleviate the heavy burden of them and produce consistent urban plans, we want to ask that can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models provides a possible answer, which inspires us to automate urban planning from an adversarial learning perspective. However, three major challenges arise: (1) how to define a quantitative land-use configuration? (2) how to automate configuration planning? (3) how to evaluate the quality of a generated configuration? In this article, we systematically address the three challenges. Specifically, (1) We define a land-use configuration as a longitude-latitude-channel tensor. (2) We formulate the automated urban planning problem into a task of deep generative learning. The objective is to generate a configuration tensor given the surrounding contexts of a target region. In particular, we first construct spatial graphs using geographic and human mobility data crawled from websites to learn graph representations. We then combine each target area and its surrounding context representations as a tuple, and categorize all tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Next, we develop an adversarial learning framework, in which a generator takes the surrounding context representations as input to generate a land-use configuration, and a discriminator learns to distinguish between positive and negative samples. (3) We provide quantitative evaluation metrics and conduct extensive experiments to demonstrate the effectiveness of our framework.