Security and Privacy Challenges in Deep Learning Models

TL;DR

Deep learning models, while highly successful across various fields, face significant security and privacy threats from attacks like model extraction, inversion, adversarial, and data poisoning, which compromise their integrity and confidentiality.

Contribution

This paper provides a comprehensive overview of security and privacy challenges in deep learning, highlighting attack types and their impact on model integrity and data confidentiality.

Findings

Deep learning models are vulnerable to multiple attack types.

Attacks can occur during training and testing phases.

Security measures are essential for protecting models and data.

Abstract

These days, deep learning models have achieved great success in multiple fields, from autonomous driving to medical diagnosis. These models have expanded the abilities of artificial intelligence by offering great solutions to complex problems that were very difficult to solve earlier. In spite of their unseen success in various, it has been identified, through research conducted, that deep learning models can be subjected to various attacks that compromise model security and data privacy of the Deep Neural Network models. Deep learning models can be subjected to various attacks at different stages of their lifecycle. During the testing phase, attackers can exploit vulnerabilities through different kinds of attacks such as Model Extraction Attacks, Model Inversion attacks, and Adversarial attacks. Model Extraction Attacks are aimed at reverse-engineering a trained deep learning model, with the primary objective of revealing its architecture and parameters. Model inversion attacks aim to compromise the privacy of the data used in the Deep learning model. These attacks are done to compromise the confidentiality of the model by going through the sensitive training data from the model's predictions. By analyzing the model's responses, attackers aim to reconstruct sensitive information. In this way, the model's data privacy is compromised. Adversarial attacks, mainly employed on computer vision models, are made to corrupt models into confidently making incorrect predictions through malicious testing data. These attacks subtly alter the input data, making it look normal but misleading deep learning models to make incorrect decisions. Such attacks can happen during both the model's evaluation and training phases. Data Poisoning Attacks add harmful data to the training set, disrupting the learning process and reducing the reliability of the deep learning mode.