A Per-Bag Suspicion-Based Bagging Strategy for Fighting Poisoning Attacks in Classification
Bibliographic record
Abstract
The wide adoption of machine learning-powered systems in sensitive applications, such as banking for fraud detection, has attracted malicious actors who seek to break and subvert these systems. In this work, we focus on Data Poisoning attacks, which is a well-known type of adversarial attack carried out by an adversary whose goal is to reduce the effectiveness of the learning system. Bagging, a well-known ensemble learning technique that aims to improve performance and reduce the overall system variance, has demonstrated robustness against data poisoning attacks. Bagging has been further extended to include weighted schemes designed to detect outliers and assign lower resampling probabilities to anomalous instances, thereby enhancing the robustness of the standard bagging mechanism. Weighted bagging significantly improves system performance when the dataset is poisoned; however, it often suffers from instability due to the mechanism used to estimate the resampling probabilities. To address this challenge, we propose a novel weight estimation approach that leverages the reconstruction capabilities of autoencoders to identify and down-weight anomalous training samples. In particular, we investigate a specific type of data poisoning attack known as a label-flipping attack, using the widely studied MNIST dataset of handwritten images and conduct experiments using a Convolutional Neural Network (CNN). Our results show that the proposed weighted bagging mechanism consistently outperforms standard bagging under data poisoning levels of up to $50 \%$. To our knowledge, this is the first study to introduce a per-bag anomaly-based weighting mechanism, paving the way for future adaptive ensemble defenses in adversarial machine learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".