Adversarial Robustness Evaluation
Comprehensive evaluation of AI model robustness using AdvGLUE, TextFooler, and other adversarial attack benchmarks
What is Adversarial Robustness in AI?
Adversarial robustness measures how well AI models maintain correct predictions when faced with deliberately crafted inputs designed to fool them. Unlike standard evaluation that tests performance on clean data, adversarial evaluation uses modified inputs that appear similar to humans but cause models to make incorrect predictions. This is crucial for deploying AI systems in real-world environments where they may encounter malicious or unexpected inputs.
AdvGLUE: Adversarial GLUE Benchmark
Benchmark Characteristics
- 14 adversarial attack types across GLUE tasks
- Multiple attack strategies: Character, word, sentence, and semantic-level perturbations
- Natural language tasks: Classification, entailment, similarity, question answering
- Automatic attack generation using various algorithms
- Human evaluation for semantic preservation
- Attack success rate and robustness metrics
Attack Categories
- Character-level: Typos, character swaps, visual similarity
- Word-level: Synonym substitution, word insertion/deletion
- Sentence-level: Paraphrasing, sentence reordering
- Semantic-level: Meaning-preserving transformations
- Syntactic: Grammar-preserving structural changes
- Style-based: Formality, tone, dialect variations
AdvGLUE Attack Examples
Original: "The movie was absolutely terrible."
Adversarial: "The m0vie was abs0lutely terrib1e."
Original: "The movie was absolutely terrible."
Adversarial: "The film was completely awful."
Original: "Although the acting was good, the plot was confusing."
Adversarial: "The plot was confusing, despite the good acting."
Original: "The restaurant serves excellent Italian food."
Adversarial: "This establishment offers outstanding cuisine from Italy."
AdvGLUE Tasks & Metrics
Covered Tasks
- • SST-2: Sentiment analysis robustness
- • MNLI: Natural language inference attacks
- • QQP: Question similarity adversarial examples
- • QNLI: Question answering robustness
- • RTE: Textual entailment attacks
- • WNLI: Winograd schema challenges
Evaluation Metrics
- • Attack Success Rate: % of successful adversarial examples
- • Robustness Score: 1 - attack success rate
- • Semantic Similarity: Preservation of meaning
- • Perplexity Score: Naturalness of adversarial text
- • Edit Distance: Minimal changes required
- • Query Efficiency: Number of queries needed
TextFooler: Word-Level Adversarial Attacks
Attack Methodology
- Word importance ranking: Identify critical words for prediction
- Synonym substitution: Replace words with semantically similar alternatives
- Part-of-speech preservation: Maintain grammatical correctness
- Semantic consistency: Ensure meaning preservation using sentence encoders
- Black-box attacks: Only requires model predictions, not gradients
- Minimal perturbations: Change as few words as possible
Algorithm Steps
- Word Importance: Rank words by their impact on model prediction
- Synonym Generation: Find semantically similar words using embedding models
- Filtering: Remove synonyms that change part-of-speech or semantics
- Substitution: Replace words in order of importance until attack succeeds
- Verification: Ensure semantic similarity > threshold (typically 0.84)
- Optimization: Minimize number of word changes needed
TextFooler Attack Process
Original: "This movie was absolutely terrible."
Importance Scores: terrible (0.95), movie (0.82), absolutely (0.41), was (0.12), this (0.05)
Target word: "terrible" (highest importance)
Candidates: awful, dreadful, horrible, bad, poor
Selected: "awful" (maintains POS, semantic similarity = 0.89)
Adversarial: "This movie was absolutely awful."
Original Prediction: Negative (confidence: 0.92)
New Prediction: Positive (confidence: 0.67) ← Attack Success!
Other Adversarial Attack Methods
BERT-Attack
Uses BERT's masked language modeling to generate contextually appropriate word replacements that fool target models.
- • Contextual word replacement using BERT
- • Maintains syntactic and semantic correctness
- • Higher attack success rates than synonym-based methods
BAE (BERT-based Adversarial Examples)
Combines BERT masking with USE (Universal Sentence Encoder) similarity constraints for semantic preservation.
- • BERT-based token replacement
- • USE similarity for semantic filtering
- • Both insertion and substitution attacks
PWWS (Probability Weighted Word Saliency)
Computes word saliency based on prediction probability changes and replaces important words with WordNet synonyms.
- • Probability-based word importance
- • WordNet synonym database
- • Semantic similarity constraints
Genetic Algorithm Attacks
Uses evolutionary optimization to find minimal perturbations that successfully fool target models.
- • Population-based optimization
- • Multi-objective fitness (attack success + minimal changes)
- • Good for finding diverse attack patterns
Adversarial Robustness Performance
| Model | Clean Accuracy | TextFooler ASR | BERT-Attack ASR | Robustness Score |
|---|---|---|---|---|
| BERT-Large | 94.2% | 84.5% | 89.2% | 0.13 |
| RoBERTa-Large | 95.8% | 81.2% | 86.7% | 0.17 |
| GPT-3.5 | 89.1% | 71.3% | 76.8% | 0.26 |
| GPT-4 | 92.4% | 58.9% | 64.2% | 0.38 |
| Claude-3 Opus | 91.7% | 52.4% | 57.1% | 0.45 |
Key Observations
- • Larger models tend to be more vulnerable to adversarial attacks
- • High clean accuracy doesn't guarantee adversarial robustness
- • Modern LLMs show better robustness than BERT-based models
- • Constitutional AI training improves adversarial resistance
Vulnerability Patterns
- • Sentiment analysis most vulnerable to word substitution
- • NLI tasks susceptible to syntactic restructuring
- • Question answering vulnerable to context manipulation
- • Classification tasks easier to attack than generation tasks
Adversarial Defense Strategies
Adversarial Training
- • Include adversarial examples in training data
- • Online adversarial training during model updates
- • Curriculum learning from simple to complex attacks
- • Multi-attack adversarial training for robustness
Input Preprocessing
- • Spell checking and grammar correction
- • Synonym normalization and standardization
- • Paraphrase-based input reconstruction
- • Adversarial example detection and filtering
Model Architecture
- • Ensemble methods for consensus predictions
- • Randomized smoothing during inference
- • Certified defense mechanisms
- • Robust optimization objectives
Detection & Monitoring
- • Real-time adversarial example detection
- • Confidence-based uncertainty estimation
- • Statistical anomaly detection in inputs
- • Human-in-the-loop verification for suspicious inputs
Adversarial Evaluation Implementation
Framework Features
- • Multiple attack algorithms (TextFooler, BERT-Attack, BAE)
- • Comprehensive robustness metrics and analysis
- • Semantic similarity preservation verification
- • Attack success rate and query efficiency tracking
- • Visualization tools for adversarial examples
- • Defense mechanism evaluation capabilities
Future Directions in Adversarial Robustness
Advanced Attack Methods
- • Multi-modal adversarial attacks (text + images)
- • Cross-lingual adversarial examples
- • Temporal adversarial sequences
- • Physically realizable attacks
Emerging Applications
- • Adversarial robustness in dialogue systems
- • Code generation adversarial examples
- • Medical text robustness evaluation
- • Legal document adversarial testing
Defense Innovations
- • Certified robustness guarantees
- • Adaptive defense mechanisms
- • Robustness-accuracy trade-off optimization
- • Zero-shot adversarial defense
Evaluation Standards
- • Standardized robustness benchmarks
- • Human evaluation of adversarial quality
- • Robustness certification frameworks
- • Industrial robustness testing protocols