Adversarial Vision

Adversarial training is a technique used to improve the robustness of computer vision models by training them on adversarial examples. These examples are specifically designed to mislead the model into making incorrect predictions. According to a recent study, the integration of vision capabilities in foundation models can introduce adversarial examples, which can be used as an attack vector.

What is Adversarial Training?

Adversarial training is a type of training where the model is trained on a dataset that includes adversarial examples. This helps the model to learn to recognize and withstand these types of attacks. As discussed in Adversarial Training Essentials, adversarial training is a crucial aspect of developing robust computer vision algorithms.

A 2024 study provides a modern re-examination of adversarial training, investigating its potential benefits when applied at scale. The study introduces an efficient and effective training strategy to enable adversarial training with giant models and web-scale data at an affordable computing cost.

Types of Adversarial Attacks

There are several types of adversarial attacks that can be used to test the robustness of computer vision models. These include:

• Image classification attacks
• Object detection attacks
• Semantic segmentation attacks
• Image-to-text attacks

A recent survey gives in-depth coverage of state-of-the-art defense strategies proposed recently to counter these attacks.

Benefits of Adversarial Training

Adversarial training has several benefits, including:

• Improved robustness: Adversarial training helps to improve the model's ability to withstand adversarial attacks.
• Increased accuracy: Adversarial training can also help to improve the model's accuracy on clean data.
• Enhanced security: Adversarial training can help to protect the model from malicious attacks.

A recent paper proposes a data-augmented virtual adversarial training approach called MixVAT, which is able to enhance the robustness of pre-trained vision transformers against adversarial attacks.