FIG.01
Data Poisoning
Training set contamination leading to targeted misclassification bounds. Analysis of injection vectors, backdoor mechanisms, and SRE mitigation strategies.
Poison Rate %
0.05% Threshold
Model Accuracy Delta
-14.2% Impact
Backdoor Success
98.5% Triggered
Attack Surface
PHASE 01
Data Collection
VULN: Web scraping unverified sources
PHASE 02
Labeling
VULN: Crowdsourced annotator bias/malice
PHASE 03
ETL Pipeline
SECURE: Hash verification active
PHASE 04
Model Training
IMPACT: Poisoned weights finalized
Mechanism
01
Poison Patterns
Introduction of specific feature correlations (e.g., specific pixel blocks, text syntax) into the training distribution.
> [x_trigger, y_target] ∈ D_train
02
Label Flips
Direct modification of ground truth labels for specific classes to degrade overall accuracy or target specific outputs.
Y_true: 'Benign'
Y_poison: 'Malicious'
03
Backdoors
Model learns to associate the trigger pattern with the target class, remaining dormant until activated during inference.
Normal Input
+
Trigger
=
Target Class
Detection & Mitigation
Vector
Monitoring Signal
Control Measure
Data Source
Hash Mismatch
Cryptographic Provenance
Enforce signed data manifests
Labeling
High Annotator Variance
Multi-pass Consensus
Require 3+ annotators per critical sample
Features
Activation Clustering
Spectral Signatures
Robust Statistics
Filter outliers in latent space before training
Model
Unusually Low Loss
Differential Privacy
Gradient clipping & noise injection (DP-SGD)