Adversarial Machine Learning for Social Good: Reframing the Adversary as
  an Ally
Shawqi Al-Maliki Adnan Qayyum Hassan Ali Mohamed Abdallah Junaid Qadir Dinh Thai Hoang Dusit Niyato Ala Al-Fuqaha
Deep Neural Networks (DNNs) have been the driving force behind many of the
recent advances in machine learning. However, research has shown that DNNs are
vulnerable to adversarial examples -- input samples that have been perturbed to
force DNN-based models to make errors. As a result, Adversarial Machine
Learning (AdvML) has gained a lot of attention, and researchers have
investigated these vulnerabilities in various settings and modalities. In
addition, DNNs have also been found to incorporate embedded bias and often
produce unexplainable predictions, which can result in anti-social AI
applications. The emergence of new AI technologies that leverage Large Language
Models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing
anti-social applications at scale. AdvML for Social Good (AdvML4G) is an
emerging field that repurposes the AdvML bug to invent pro-social applications.
Regulators, practitioners, and researchers should collaborate to encourage the
development of pro-social applications and hinder the development of
anti-social ones. In this work, we provide the first comprehensive review of
the emerging field of AdvML4G. This paper encompasses a taxonomy that
highlights the emergence of AdvML4G, a discussion of the differences and
similarities between AdvML4G and AdvML, a taxonomy covering social good-related
concepts and aspects, an exploration of the motivations behind the emergence of
AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the
works that utilize AdvML4G as an auxiliary tool for innovating pro-social
applications. Finally, we elaborate upon various challenges and open research
issues that require significant attention from the research community.