A survey on adversarial attacks and defences

By Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, Debdeep Mukhopadhyay. CAAI Transactions on Intelligence Technology, 2021

457 citations recorded by the source corpus.

Deep learning has evolved as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. The advancement of deep learning has been so radical that today it can surpass human ‐ level performance. As a consequence, deep learning is being extensively used in most of the recent day ‐ to ‐ day applications. However, efficient deep learning systems can be jeopardised by using crafted adversarial samples, which may be imperceptible to the human eye, but can lead the model to misclassify the output. In recent times, different types of adversaries based on their threat model leverage these vulnerabilities to compromise a deep learning system where ad-versaries have high incentives. Hence, it is extremely important to provide robustness to deep learning algorithms against these adversaries. However, there are only a few strong countermeasures which can be used in all types of attack scenarios to design a robust deep learning system. Herein, the authors attempt to provide a detailed discussion on different types of adversarial attacks with various threat models and also elaborate on the efficiency and challenges of recent countermeasures against them

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a06a8ca70096e567e5cf1e433cc99ac1d519c4d0

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Citations in the corpus

Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling (2019, background): “For more comprehensive overviews of recent advancements in adversarial attacks, we direct the interested reader to survey papers for adversarial attacks in the image [1], text [25], and graph domains [23].”
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations (2019, background): “Generating adversarial examples has received increasing attention in natural language processing [31, 30].”
Generalizable Adversarial Attacks Using Generative Models (2019, background): “[6, 38], represents a significant challenge [35, 32].”