In recent years, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in the field of target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any information about SAR-ATR models, making adversarial attacks challenging. In this situation, we propose Transferable Adversarial Network (TAN) to attack these models with highly transferable adversarial examples. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder-decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. In particular, compared to traditional iterative methods, our approach is able to one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that the proposed approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, i.e., it excellently realizes real-time transferable adversarial attacks.

Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks are easily failed in the case where adversarial perturbations cannot be fully fed to victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of the areas in SAR images and has low relevance to recognition results, fooling models with global perturbations is quite inefficient. This paper proposes a semi-whitebox attack network, called Universal Local Adversarial Network (ULAN), to generate universal adversarial perturbations (UAP) for the target regions of SAR images. In the proposed network, we calculate the model’s attention heatmaps through layer-wise relevance propagation (LRP), which is used to locate the target regions of SAR images that have high relevance to recognition results. In particular, we utilize a generator based on the U-Net to learn the mapping from noise to UAPs and craft adversarial examples by adding the generated local perturbations to target regions. Experiments indicate that the proposed method fundamentally prevents perturbation offset and achieves comparable attack performance to conventional global UAPs by perturbing only a quarter or less of SAR image areas.

In 2015, the Self-Action Leadership Theory—a qualitative, nomological expansion of self-leadership theory rooted in atmospheric and astronomical metaphor aimed at expanding the personal freedom of individuals, organizations, and nations by bolstering the existential growth of individuals through a series of Maslow-esque stages of holistic, personal development. This article introduces an accompanying, practitioner-based Model of Self-Action Leadership (SAL) aimed at the implicit enhancement of a holistic range of administrative processes through explicit training, mentoring, and coaching in the model’s general and universally-applicable principles and practices. The SAL model produces an original construct of personal leadership practice that builds upon the extant self-leadership academic canon, which dates back to 1983 (Manz, 1983). It also provides an analogue to four of the five core processes of Project Management by positioning a self-action leader (an individual) as the ongoing “project” at hand. The SAL Model is rooted in action research and was developed through a variety of self-oriented, action research projects in conjunction with a comprehensive, qualitative, analytical autoethnographic study of a scholar’s life experiences.