Real World Trade-offs: When to Use Adversarial Defenses vs Alternatives

Adversarial training and robust defenses come with steep costs that make them inappropriate for many systems. Understanding when to invest in robustness versus simpler alternatives is a critical production decision that balances security, latency, and cost. Use adversarial training for high stakes, high throughput decisions where attackers can probe cheaply and gains from evasion are large. Payment fraud detection is a textbook example: attackers can test stolen cards at near zero cost, successful evasion yields direct financial gain, and the system serves 50,000 to 500,000 requests per second where 0.1% evasion rate translates to millions in losses. Here, accepting 3 to 4x training cost increases and 2 to 3 percentage point accuracy drops is justified. Similarly, content moderation at Meta scale where policy violators continuously probe classifiers with paraphrased harmful content warrants robust training despite compute costs. Avoid heavy adversarial defenses when response latency must stay under 10 milliseconds or when compute budgets are tight. Lightweight anomaly detection using simple statistical methods (z score based outlier detection on feature distributions) or business rules (hard limits on transaction velocity, geographic impossibility checks) provide reasonable protection at 1 to 3 milliseconds latency. For moderate risk applications like recommendation ranking where attack impact is limited to user experience degradation rather than financial loss, simpler diversity injection or randomization provides sufficient resilience without robustness overhead. Certified defenses using randomized smoothing or interval bound propagation provide provable guarantees but are almost never practical for online serving. Randomized smoothing requires 32 to 256 noisy forward passes per prediction, inflating latency by that factor. A 20 millisecond model becomes 640 milliseconds to 5 seconds per request, completely infeasible for user facing services. Use certified methods offline to quantify risk and set security budgets, then deploy empirical defenses like adversarial training for actual serving. The key insight is that model robustness is one layer in defense in depth. Rate limiting prevents cheap boundary probing regardless of model robustness. Manual review queues for the top 0.1 to 1% riskiest cases catch sophisticated attacks that bypass models. Multi layer defenses at different stages (pre ingestion filters, online scoring, post processing audits) force attackers to evade multiple independent checks. Honeypot accounts and decoy features help detect probing attempts. In many systems, investing in these complementary defenses provides better security per dollar than making models arbitrarily robust.

Stripe payment fraud (high stakes): Adversarial training justified despite 4x cost increase and 2.1% accuracy drop. At $50 billion annual payment volume, 0.1% evasion rate reduction saves $50 million in fraud losses, far exceeding $100,000 annual training cost increase.

Mobile app recommendation (moderate risk): Skips adversarial training, uses diversity injection and randomization instead. Attack impact limited to slightly worse user experience, not financial loss. Saves 300ms model load time on device and 50MB model size.

Amazon marketplace abuse (layered defense): Pre ingestion rules (1ms) catch 80% of obvious abuse. Online scoring (30ms fast path) catches 15% more. Post publication audits (offline batch) catch final 5%. Attacker must evade all three layers, each using different detection logic.