Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start

E-commerce platforms provide their customers with ranked lists of recommended items matching the customers' preferences. Merchants on e-commerce platforms would like their items to appear as high as possible in the top-N of these ranked lists. In this paper, we demonstrate how unscrupulous merchants can create item images that artificially promote their products, improving their rankings. Recommender systems that use images to address the cold start problem are vulnerable to this security risk. We describe a new type of attack, Adversarial Item Promotion (AIP), that strikes directly at the core of Top-N recommenders: the ranking mechanism itself. Existing work on adversarial images in recommender systems investigates the implications of conventional attacks, which target deep learning classifiers. In contrast, our AIP attacks are embedding attacks that seek to push features representations in a way that fools the ranker (not a classifier) and directly lead to item promotion. We introduce three AIP attacks insider attack, expert attack, and semantic attack, which are defined with respect to three successively more realistic attack models. Our experiments evaluate the danger of these attacks when mounted against three representative visually-aware recommender algorithms in a framework that uses images to address cold start. We also evaluate two common defenses against adversarial images in the classification scenario and show that these simple defenses do not eliminate the danger of AIP attacks. In sum, we show that using images to address cold start opens recommender systems to potential threats with clear practical implications. To facilitate future research, we release an implementation of our attacks and defenses, which allows reproduction and extension.Comment: Our code is available at https://github.com/liuzrcc/AI

Practical Deep Reinforcement Learning Approach for Stock Trading

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns

Simulation analysis of UAV autonomous landing system based on TECs

Aiming at the decoupling control problem of velocity and altitude in the process of unmanned aerial vehicles (UAV) autonomous landing under visual guidance, this paper establishes the fl ight control model of fi xed wing UAV, and deduces the coupling relationship between airspeed and altitude in the process of UAV glide, The total energy control system (TECs) is used for decoupling control to realize the autonomous fi xed-point landing of UAV. The simulation results show that the designed control law can decouple the airspeed and altitude of the UAV, so that the UAV can land at the predetermined place autonomously and accurately