Detection of Anomalous Data in Vision Models Using Statistical Techniques

Concepts de base

Benford's law can be used as a filter for anomalous data points and out-of-distribution data, aiding in model robustness and monitoring.

Résumé

Introduction to the challenges of deploying machine learning systems.
Importance of detecting anomalies and out-of-distribution data.
Utilization of Benford's law for anomaly detection.
Comparison with existing methods in literature.
Application of Benford's law to image distributions using DCT coefficients.
Testing on ImageNet-C dataset for various corruption types.
Results showing divergence from Benford's law with different corruption severities.
Limitations and potential future directions.
Conclusion on the effectiveness of the approach.

Stats

"Out-of-distribution means there is a difference in distributional properties between the test, training, and real-world data."
"Even simple shifts in data distribution can lead to a large drop in performance."
"The empirical distribution of the LDs of the DCT coefficients from each block is calculated with respect to a base, e.g., base 10."

Citations

"Results show that for many corruption types, images that are corrupted to a higher level typically deviate from the expected distribution more."
"This technique could be added to the toolkit as a low computational filter for anomalous or out-of-distribution data."

Idées clés tirées de

On the Detection of Anomalous or Out-Of-Distribution Data in Vision Models Using Statistical Techniques

by Laura O'Maho... à arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15497.pdf

Questions plus approfondies

How can Benford's law be adapted for other types of datasets beyond images

Benford's law can be adapted for other types of datasets beyond images by considering the underlying distribution of the data. The key is to identify a natural pattern in the leading digits or other statistical properties that should follow Benford's law if the data is authentic and unaltered. For numerical datasets, such as financial transactions, population numbers, or scientific measurements, one can analyze the frequency distribution of leading digits to detect anomalies or irregularities based on Benford's law. By applying appropriate transformations or statistical analyses specific to each type of dataset, researchers can leverage Benford's law as a tool for anomaly detection across various domains.

What are the limitations when using statistical techniques like Benford's law for anomaly detection

While statistical techniques like Benford's law offer valuable insights into detecting anomalies in datasets, they also come with limitations. One limitation is that Benford's law assumes a certain distribution pattern for naturally occurring data; however, not all datasets may conform to this pattern perfectly. In cases where the data deviates significantly from what is expected under Benford's law, false positives or false negatives may occur during anomaly detection. Additionally, outliers and extreme values within a dataset can skew results when using statistical methods like Benford's law. Moreover, these techniques may not always capture complex anomalies that require more sophisticated algorithms or domain-specific knowledge for accurate detection.

How might anomalies detected by statistical methods impact model performance differently than other detection methods

Anomalies detected by statistical methods like Benford's law may impact model performance differently than other detection methods due to their focus on underlying distributions rather than specific features or patterns within the data. Statistical techniques are useful for identifying broad deviations from expected norms but may overlook subtle changes that could affect model predictions. As a result, anomalies detected through statistical analysis alone might not always correlate directly with significant drops in model performance unless those anomalies lead to drastic shifts in overall data distribution that impact model generalization capabilities negatively.