Bias and Fairness of Evasion Attacks in Image Perturbation

When talking about protecting privacy of personal images, adversarial attack methods play key roles. These methods are created to protect against the unauthorized usage of personal images. Such methods protect personal privacy by adding some amount of perturbations, otherwise known as noise , to input images to enhance privacy protection. Fawkes in Clean Attack method is one adversarial machine learning approach aimed at protecting personal privacy against abuse of personal images by unauthorized AI systems. In leveraging the Fawkes in Evasion Attack method and through running additional experiments against the Fawkes system, we were able to prove that the effectiveness of perturbations added in privacy protection of images depends on how we stratify the input population based on demographic features such as race and gender, showing that we need to be able to quantify and take into account various potential areas of bias when leveraging adversarial attack methods to ensure optimal protection of all input images. As it currently stands, the Fawkes system has a fixed set of hyper parameters for amount of perturbations added per image, which essentially means that they consider all users be treated identically in terms of amount of perturbations added. However, from testing our hypothesis through running various experiments, we found that the protection performance is statistically significantly different when the input images are from different groups of people based on demographic features like race and gender when applying the original parameter settings. For example, we found that for light skin toned females, the original Fawkes settings work well in ensuring privacy protection. However, the original Fawkes settings do not perform well with dark skin toned males in ensuring privacy protection of these images. In order to ensure fairness from the system, we propose guidelines for taking into account these demographic differences in order to get optimized solution sets for hyper parameter tuning, making future users of the model aware of existing biases and how to mitigate and take them into account. Our proposed solution for hyper parameter tuning takes into account demographic features with internal system settings, aimed at improving the protection performance for all skin tones and gender. We categorized inputs based on demographic features (namely, race and gender) and then used the current Fawkes model to process the categorized input images with different parameters. In our experiments, the main metric we use to evaluate and determine the optimal hyper parameters is the output of custom classifier models (e.g., confidence values) built from Microsoft Cognitive Services Face API. From a high-level, we first test the effectiveness of the Fawkes model applied in Evasion Attack Scenario. Then we ran experiments with curated datasets to prove the existence of demographic bias in the current Fawkes model with its default parameters. Next, we performed experiments on changing the default parameters of Fawkes to analyze the influence of different parameters on different input images. Based on the previous experiment results, we propose guidelines and solution sets that optimize the internal settings to ensure Fawkes model takes into account potential demographic biases and ensure fair protection for all input images. Our proposed solution and devised set of guidelines takes into account various demographic features (e.g., race and gender) and internal settings together by using grid-search like methods, namely pair-to-pair. By applying our proposed set of guidelines, we ensure optimal protection performance by all skin tones and gender, improving bias and enhancing fairness of the Fawkes model

Energy Optimization in Multi-UAV-Assisted Edge Data Collection System

In the IoT (Internet of Things) system, the introduction of UAV (Unmanned Aerial Vehicle) as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery energy. However, the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the system. In this work, to deal with the problem, a deployment model of a mobile edge computing (MEC) system based on multi-UAV is proposed. The goal of the model is to minimize the energy consumption of the system in the process of data transmission by optimizing the deployment of UAVs. The DEVIPSK (differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means) is proposed to solve the model. In DEVIPSK, the population is initialized by K-Means to obtain better initial positions of UAVs. Besides, considering the limitation of the fixed mutation strategy in the traditional evolutionary algorithm, a mutation strategy pool is used to update the positions of UAVs. The experimental results show the superiority of the DEVIPSK and provide guidance for the deployment of UAVs in the field of edge data collection in the IoT system

Optimization for Variable Height Wind Farm Layout Model

The optimization of wind farm layouts is very important for the effective utilization of wind resources. A fixed wind turbine hub height in the layout of wind farms leads to a low wind energy utilization and a higher LCOE (levelized cost of electricity). WOMH (Wind Farm Layout Optimization Model Considering Multiple Hub Heights) is proposed in this paper to tackle the above problem. This model is different from the traditional fixed hub height model, as it uses a variable height wind turbine. In WOMH, the Jensen wake and Weibull distribution are used to describe the wake effect on the wind turbines and wind speed distribution, respectively. An algorithm called DEGM (differential evolution and greedy method with multiple strategies) is proposed to solve WOMH, which is NP hard. In the DEGM, seven strategies are designed to adjust the distribution coordinates of wind turbines so that the height of the wind turbines will be arranged from low to high in the wind direction. This layout reduces the Jensen wake effect, thus reducing the value of the LCOE. The experimental results show that in the DEGM, when the number of wind turbines is 5, 10, 20, 30 and 50, the WOMH reduces the LCOE by 13.96%, 12.54%, 8.22%, 6.14% and 7.77% compared with the fixed hub height model, respectively. In addition, the quality of the solution of the DEGM is more satisfactory than that of the three-dimensional greedy algorithm and the DEEM (differential evolution with a new encoding mechanism) algorithm. In the case of five different numbers of wind turbines, the LCOE of DEGM is at least 3.67% lower than that of DEEM, and an average of 6.83% lower than that of three-dimensional greedy. The model and algorithm in this paper provide an effective solution for the field of wind farm layout optimization

Bias and fairness in Evasion Attack of Image Perturbation

Analysis of image perturbations in Evasion Attack scenario. Using data analysis techniques to find optimal parameters as categorized inputs for different groups of users in order to enhance the original Fawkes system