What are adversarial attacks on neural networks?

Adversarial attacks on neural networks are inputs intentionally designed to cause models to make incorrect predictions. These attacks work by applying subtle, often imperceptible modifications to input data—such as images, text, or audio—to exploit vulnerabilities in how the model processes information. For example, a small perturbation added to an image of a cat might cause a classifier to label it as a dog, even though the altered image looks identical to the original to a human. These perturbations are calculated using algorithms that identify patterns the model relies on, then tweak inputs to mislead it. The core issue is that neural networks often learn features that are not robust to these carefully crafted changes, even when the model performs well on normal data.

Attack methods vary in complexity and approach. One common technique, the Fast Gradient Sign Method (FGSM), uses the model’s gradients during training to determine how to adjust the input data efficiently. Another example is the Projected Gradient Descent (PGD) attack, which iteratively refines perturbations to maximize prediction errors. Physical-world attacks demonstrate practical risks: a sticker placed strategically on a stop sign can cause an autonomous vehicle’s object detector to misclassify it as a speed limit sign. Attacks can also target different phases of a model’s lifecycle. Evasion attacks, the most common type, occur during inference, while poisoning attacks corrupt training data to compromise the model before deployment. These methods highlight how attackers exploit specific weaknesses, whether by accessing model internals (white-box attacks) or probing inputs and outputs (black-box attacks).

Defending against adversarial attacks remains an active challenge. A widely used approach is adversarial training, where models are trained on perturbed examples to improve robustness. For instance, a classifier might be fine-tuned with images altered via FGSM to reduce sensitivity to such perturbations. Other defenses include input preprocessing—like denoising filters or spatial transformations—to remove adversarial patterns before inference. Techniques like gradient masking aim to obscure the model’s decision boundaries, making it harder for attackers to craft effective perturbations. However, many defenses are bypassed by adaptive attacks, underscoring that no solution is universally reliable. Developers must balance robustness with performance trade-offs and consider adversarial testing as part of their deployment pipeline. Understanding these attacks and defenses is critical for building trustworthy systems, especially in security-sensitive domains like healthcare or autonomous systems.