Most deep learning models are easily vulnerable to adversarial attacks. Various adversarial attacks are designed to evaluate the robustness of models and develop defense model. Currently, adversarial attacks are brought up to attack their own target model with their own evaluation metrics. And most of the black-box adversarial attack algorithms cannot achieve the expected success rate compared with white-box attacks. In this paper,comprehensive evaluation metrics are brought up for different adversarial attack methods. A novel perturbation optimized blackbox adversarial attack based on genetic algorithm (POBA-GA) is proposed for achieving white-box comparable attack performances. Approximate optimal adversarial examples are evolved through evolutionary operations including initialization, selection, crossover and mutation. Fitness function is specifically designed to evaluate the example individual in both aspects of attack ability and perturbation control. Population diversity strategy is brought up in evolutionary process to promise the approximate optimal perturbations obtained. Comprehensive experiments are carried out to testify POBA-GA's performances. Both simulation and application results prove that our method is better than current state-of-art black-box attack methods in aspects of attack capability and perturbation control.
In recent years, DC fault arc detection has been an electrical engineering research hotspot. At present, most proposed detection methods do not analyze the effects of fault arc electrical characteristics on both line current and supply voltage. Therefore, this study extensively analyzes variations of the line current and supply voltage because of DC arc faults based on the volt-ampere characteristics of DC arc faults. Then, a DC series arc fault detection method is proposed that comprehensively uses information on line current and supply voltage. An experimental platform for DC fault arc generation and detection was established using a DC-DC converter and a photovoltaic power supply as DC power supplies, and the proposed method was confirmed by experiments using this platform. Experimental results demonstrate that the proposed method can effectively distinguish arc faults and has the characteristics of clear physical meaning while maintaining a low amount of calculation. INDEX TERMS DC series arc, volt-ampere characteristic, electrical fault detection, current, voltage, chaotic characteristics, drop rate, change rate.
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