AI 용어집
인공지능 완전 사전
Adversarial Anomaly Detection
Process of identifying malicious samples designed to deceive AI models by analyzing statistical divergences from legitimate data. This method uses unsupervised learning techniques to detect abnormal patterns in the feature space.
Binary Detection Classifier
Machine learning model specifically trained to distinguish normal inputs from potentially adversarial inputs before they reach the main model. This system acts as a first line of defense by proactively filtering suspicious samples.
Adversarial Confidence Threshold
Predefined critical confidence value used to reject predictions when the model exhibits abnormally high or low uncertainty. Adversarial inputs often cause atypical probability distributions that this threshold helps identify.
Autoencoder Reconstruction Detection
Technique using autoencoders trained on legitimate data to detect attacks based on high reconstruction error of adversarial samples. Malicious inputs generally exhibit significantly higher reconstruction errors than normal data.
Distribution Divergence Detection
Method analyzing shifts between the distribution of test inputs and that of training data to identify potentially corrupted samples. This approach is based on the principle that adversarial attacks create abnormal distributions in the latent space.
Adversarial Detection Score
Quantitative metric evaluating the probability that an input is adversarial based on multiple indicators such as gradient magnitude and model sensitivity. This composite score enables nuanced decision-making on the malicious or legitimate nature of a sample.
Gradient Analysis Detection
Technique examining the characteristics of the loss function gradient to identify subtle adversarial perturbations. Attacks often generate gradients with distinctive statistical properties detectable by this method.
Cascade Detection System
Multi-level architecture where several specialized detectors execute sequentially to identify different types of adversarial attacks. Each level progressively filters threats while minimizing false positives on legitimate data.
Mahalanobis Distance Detection
Statistical measure evaluating how far a sample lies from the distribution of legitimate data in the feature space. Points distant according to this weighted metric are likely to be adversarial attacks.
Semantic Consistency Validation
Process verifying the logical coherence between input features to detect inconsistencies introduced by attacks. Adversarial samples often exhibit subtle semantic contradictions between different parts of the data.
Meta-Learning Detection
Approach where a meta-model learns to recognize attack patterns by training on various types of adversarial examples generated by different methods. This technique offers improved generalization against unknown attacks.
Adversarial Defense Calibration
Process of adjusting detection parameters to optimize the trade-off between detection rate and false positive rate according to the application context. Calibration ensures effective protection without excessively degrading performance on legitimate data.
Sensitivity Analysis Detection
Method evaluating how small perturbations affect model output to identify abnormally sensitive inputs. Adversarial samples generally show disproportionate sensitivity compared to normal data.
Hybrid Defense System
Combination of multiple complementary detection techniques to improve overall robustness against various types of adversarial attacks. This synergistic approach exploits the strengths of each method while compensating for their respective weaknesses.
Manifold Validation Detection
Technique verifying whether a sample lies on the manifold of legitimate data learned during model training. Adversarial attacks often create points significantly deviating from this data manifold.
Dynamic Detection Threshold
Adaptive mechanism automatically adjusting detection thresholds based on real-time data characteristics to optimize performance. This approach maintains constant efficiency in the face of evolving data distributions.
Détection par Analyse de Perturbation
Méthode identifiant les patterns de perturbation caractéristiques des attaques adversariales en analysant les modifications appliquées aux entrées originales. Cette technique se concentre sur la structure des changements plutôt que sur les valeurs absolues.
Détection par Analyse de Consistance
Technique vérifiant la cohérence des prédictions du modèle sous différentes transformations ou conditions pour identifier les comportements adversariaux. Les attaques provoquent souvent des inconsistances révélatrices sous des variations mineures de l'entrée.