While the fundamental concepts of adversarial attacks and defenses apply broadly, their practical application and effectiveness are heavily influenced by the specific domain in which the machine learning model operates. The nature of the data, the model's task, and the definition of a meaningful perturbation vary significantly between areas like computer vision, natural language processing, and reinforcement learning. Understanding these domain-specific nuances is essential for developing relevant attack strategies and robust defenses.

Computer Vision (CV) Considerations

In computer vision, models typically process continuous, high-dimensional data like pixel values.

Perturbation Constraints: The most common mathematical constraint for perturbations in CV is the $L_p$ norm, particularly $L_\infty$ and $L_2$ . The goal is usually to find the smallest perturbation under a given norm that causes misclassification. However, the ultimate constraint is often perceptual similarity. A small $L_\infty$ perturbation might still be visually noticeable, especially in smooth image regions, while a larger one might be hidden in textured areas. Metrics like Structural Similarity Index (SSIM) sometimes capture human perception better than simple $L_p$ norms. Crafting imperceptible attacks often requires careful consideration of the human visual system.
Attack Goals: Beyond simple misclassification of an entire image, attacks might target object detectors (causing objects to be missed or spurious objects detected), semantic segmentation models (corrupting pixel-level labels), or facial recognition systems. The definition of "successful attack" depends heavily on the specific CV task.
Physical World Attacks: Creating adversarial examples that remain effective when printed and captured by a camera introduces significant challenges. The attack must be robust to variations in lighting, angle, distance, camera sensor noise, and the printing/display process itself. This often requires specialized optimization techniques, such as Expectation Over Transformation (EOT), to create attacks resilient to these real-world variations.

Natural Language Processing (NLP) Considerations

NLP models operate on discrete data (characters, words, tokens), which fundamentally changes how attacks are constructed.

Input Space: The discrete nature of text means that gradient-based methods developed for continuous inputs cannot be directly applied to modify tokens. Perturbations involve changing discrete units: characters, words, or sentences.
Perturbation Constraints: The primary constraint is semantic preservation. An adversarial text sample should ideally retain the original meaning and grammatical structure while fooling the model. Simply swapping words based on embedding similarity might result in nonsensical or grammatically incorrect text. Techniques involve:
- Synonym Substitution: Replacing words with synonyms that minimally alter meaning.
- Paraphrasing: Rewriting sentences or phrases.
- Character-Level Edits: Introducing typos, adding/removing spaces (often effective against character-based models).
- Word Insertion/Deletion: Carefully adding or removing words to shift model predictions.
Attack Goals: Common goals include flipping sentiment analysis predictions, changing topic classifications, altering machine translation outputs, causing chatbots to generate undesirable content, or bypassing content filters.
Measuring Perturbation: Instead of $L_p$ norms, perturbation size is often measured by edit distance (character or word level) or semantic similarity scores derived from language models or embedding comparisons. The number of altered words is a simple, interpretable metric.

Relevance of common perturbation metrics varies significantly between computer vision and natural language processing tasks.

Reinforcement Learning (RL) Considerations

Adversarial attacks in RL target the agent's decision-making process, which operates within an environment loop.

Attack Surface: Attacks can target the agent's observations (similar to CV if the input is visual), the reward signal (misleading the agent about task success), or the environment dynamics itself if the attacker has control over it (less common in standard threat models).
Attack Goals: The aim is often to degrade the agent's performance, causing it to learn a suboptimal policy, reach undesirable states (e.g., unsafe conditions), or fail to achieve its objective. Attacks might be targeted (force a specific wrong action) or untargeted (simply reduce overall reward).
Sequential Nature: Attacks may need to be applied persistently over multiple time steps to significantly influence the agent's behavior or learning trajectory. The effect of a single perturbed observation might be minor, but cumulative perturbations can derail the policy.
Challenges: Evaluating robustness is complex due to the interaction loop. The long-term consequences of an attack can be hard to predict. Defenses often focus on robust policy optimization or detecting anomalies in observations or rewards.

General Application Context

Beyond the data modality, the specific application context shapes adversarial considerations:

Model Architecture: Different architectures (e.g., CNNs, RNNs, Transformers) exhibit varying sensitivities to adversarial perturbations. Understanding architectural properties can inform attack design.
Data Preprocessing: Normalization, tokenization, feature scaling, and other preprocessing steps can influence attack effectiveness and how perturbations need to be crafted. Attacks might need to operate in the pre-processed space or be designed to survive the preprocessing pipeline.
Real-world Impact: The acceptable level of robustness depends critically on the application. An attack causing misclassification in a movie recommendation system has far lower stakes than one affecting an autonomous vehicle's perception system or a medical diagnosis tool. Security requirements must align with potential real-world harm.

In summary, designing effective adversarial attacks or defenses requires moving beyond generic methods and carefully considering the unique characteristics of the data, task, model, and application domain. A successful $L_\infty$ attack on an image classifier provides little direct insight into how to construct a meaning-preserving attack on a text summarization model. Tailoring strategies to these specific contexts is fundamental to understanding and mitigating adversarial risks in deployed machine learning systems.