Backdoor Attacks on Crowd Counting

Crowd counting is a regression task that estimates the number of people in a scene image, which plays a vital role in a range of safety-critical applications, such as video surveillance, traffic monitoring and flow control. In this paper, we investigate the vulnerability of deep learning based crowd counting models to backdoor attacks, a major security threat to deep learning. A backdoor attack implants a backdoor trigger into a target model via data poisoning so as to control the model's predictions at test time. Different from image classification models on which most of existing backdoor attacks have been developed and tested, crowd counting models are regression models that output multi-dimensional density maps, thus requiring different techniques to manipulate. In this paper, we propose two novel Density Manipulation Backdoor Attacks (DMBA$^{-}$ and DMBA$^{+}$) to attack the model to produce arbitrarily large or small density estimations. Experimental results demonstrate the effectiveness of our DMBA attacks on five classic crowd counting models and four types of datasets. We also provide an in-depth analysis of the unique challenges of backdooring crowd counting models and reveal two key elements of effective attacks: 1) full and dense triggers and 2) manipulation of the ground truth counts or density maps. Our work could help evaluate the vulnerability of crowd counting models to potential backdoor attacks.Comment: To appear in ACMMM 2022. 10pages, 6 figures and 2 table

How do emotions affect giving? Examining the effects of textual and facial emotions in charitable crowdfunding

Abstract Drawing on emotional contagion theory and language-mediated association theory, this study develops a research model to examine how textual and facial emotions affect charitable crowdfunding performance. We use computer-aided techniques to extract and measure specific textual and facial emotions in pitches. The proposed model is tested via regression analysis with a sample of 1372 campaigns collected from the largest charitable crowdfunding platform in China—Tencent Gongyi. Moreover, we conducted a fuzzy-set qualitative comparative analysis to examine the complementarity of textual and facial emotions, which supplements the regression analysis results. Our findings show that both textual and facial emotions can impact funding outcomes. However, the effects of specific emotions vary: some (e.g., textual sadness and facial anger) are positive, some (e.g., textual anger and facial fear) are negative, and others (e.g., textual fear, textual disgust, and facial sadness) are insignificant. Moreover, facial emotions complement textual emotions in their effects on funding outcomes. This research outlines a framework to offer a more detailed and comprehensive understanding of emotions in charitable crowdfunding. It also contributes to existing research by revealing the vital but complex role of emotions in the persuasive process of prosocial behaviors and by uncovering the different cognitive mechanisms underlying the impacts of textual and facial emotions

Exploring the Relationship between Host Self-Description and Consumer Purchase Behavior Using a Self-Presentation Strategy

Information on short-term rental platforms plays an important role in consumer purchase behavior. However, information asymmetry between host and guest has been identified as a problem in sharing economy contexts. In this paper, to fill this gap, the authors develop six hypotheses about the focal impacts of self-presentation strategy and the moderating effects of third-party certification. Based on data from Airbnb, the authors first examine how the tactics of self-presentation strategy influence consumer purchase behavior. The results show that different self-presentation tactics affect consumer purchase behavior differently. The authors also found that the third-party certification level weakens the influence of self-presentation strategy interactions on consumer purchase behavior

Fast acquisition method using modified PCA with a sparse factor for burst DS spread-spectrum transmission

To improve the acquisition speed and inbound capacity of the ground station in a burst direct-sequence (DS) spread-spectrum transmission system, an acquisition method based on a modified parallel code-phase acquisition (PCA) scheme is proposed. By taking advantage of the sparsity of the acquisition result with PCA in the time domain, we introduce the sparse factor to handle the signals via sparsification and apply the sparse recovery algorithm to search and estimate the acquisition result. The computational complexity, mean acquisition time, and relationship between the inbound capacity and acquisition performance are provided. We theoretically analyse the effect of the sparse factor on the acquisition performance. The estimation errors verify our analysis, and simulations show that the acquisition time of our proposed method outperforms that of advanced PCA by 1.2–4.0 times; additionally, the inbound capacity increases by 6.2–36.7%

Phase Sequence Exchange Technology Based on MMC for Improving the Power Angle Stability of Power Systems

Power angle stability of power systems is one of the main factors which restricts the power transmission capacity. In order to prevent the power angle instability of power systems, phase sequence exchange (PSE) is a recently developed emergency control technique that reduces the power angle by 120°. This paper proposes a new phase sequence exchange technology based on a modular multi-level converter (MMC). In this paper, the concept of deceleration area ratio is proposed, that is used to characterize the power angle stability of the power system, and the phase sequence exchange theory based on an MMC is analyzed. A phase sequence exchange circuit based on a modular multilevel converter (MMC) is designed. In this paper, the system parameters of the MMC-based PSE are optimized. Finally, a model is developed on PSCAD. The simulation results show that the MMC-based phase sequence exchange technology can effectively improve the power angle stability of the power system

Phase Sequence Exchange Technology Based on MMC for Improving the Power Angle Stability of Power Systems

Power angle stability of power systems is one of the main factors which restricts the power transmission capacity. In order to prevent the power angle instability of power systems, phase sequence exchange (PSE) is a recently developed emergency control technique that reduces the power angle by 120°. This paper proposes a new phase sequence exchange technology based on a modular multi-level converter (MMC). In this paper, the concept of deceleration area ratio is proposed, that is used to characterize the power angle stability of the power system, and the phase sequence exchange theory based on an MMC is analyzed. A phase sequence exchange circuit based on a modular multilevel converter (MMC) is designed. In this paper, the system parameters of the MMC-based PSE are optimized. Finally, a model is developed on PSCAD. The simulation results show that the MMC-based phase sequence exchange technology can effectively improve the power angle stability of the power system