| { |
| "term": "AdversarialImage", |
| "role": "concept", |
| "parent_concepts": [ |
| "MultimodalSafetyRisk" |
| ], |
| "layer": 4, |
| "domain": "SafetyAndSecurity", |
| "definition": "Images with carefully crafted perturbations designed to fool computer vision models while appearing normal to humans", |
| "definition_source": "SUMO", |
| "aliases": [ |
| "AdversarialExample", |
| "AdversarialPerturbation", |
| "EvasionAttack" |
| ], |
| "wordnet": { |
| "synsets": [], |
| "canonical_synset": "", |
| "lemmas": [], |
| "pos": "noun" |
| }, |
| "relationships": { |
| "related": [ |
| "ImageRecognition", |
| "ObjectDetection", |
| "RobustML" |
| ], |
| "antonyms": [], |
| "has_part": [], |
| "part_of": [] |
| }, |
| "safety_tags": { |
| "risk_level": "medium", |
| "impacts": [ |
| "evasion", |
| "misclassification", |
| "safety_critical_failure" |
| ], |
| "treaty_relevant": false, |
| "harness_relevant": true |
| }, |
| "training_hints": { |
| "positive_examples": [ |
| "The adversarial image caused the classifier to misidentify a stop sign as a speed limit sign.", |
| "Imperceptible pixel perturbations can cause dramatic changes in model predictions.", |
| "Adversarial patches can be printed and placed in the physical world to fool detectors.", |
| "The FGSM attack generates adversarial examples using the gradient of the loss function.", |
| "Adversarial images exploit the gap between human and machine perception.", |
| "The adversarial perturbation was invisible to humans but completely fooled the model.", |
| "Adversarial images can cause misclassification in safety-critical computer vision systems.", |
| "The adversarial patch caused the object detector to completely miss the person.", |
| "Adversarial image attacks can transfer between different models trained on similar data.", |
| "The adversarial example exploited high-frequency features that humans don't perceive." |
| ], |
| "negative_examples": [ |
| "The image was unclear.", |
| "The model made an error.", |
| "The picture was confusing.", |
| "The classification was wrong.", |
| "The image was noisy.", |
| "An error occurred.", |
| "The model was confused.", |
| "The image was distorted.", |
| "The prediction was incorrect.", |
| "The model made a mistake." |
| ], |
| "disambiguation": "Intentionally crafted perturbations to fool models, not natural errors or noise" |
| }, |
| "is_category_lens": true, |
| "child_count": 0, |
| "meld_source": { |
| "meld_id": "org.hatcat/multimodal-safety@0.2.0", |
| "applied_at": "2025-12-10T20:54:17.844054Z", |
| "pack_version": "6.0.0" |
| } |
| } |
