REVIEW 1 cited by
Non-Negative Networks Against Adversarial Attacks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Adversarial attacks against neural networks are a problem of considerable importance, for which effective defenses are not yet readily available. We make progress toward this problem by showing that non-negative weight constraints can be used to improve resistance in specific scenarios. In particular, we show that they can provide an effective defense for binary classification problems with asymmetric cost, such as malware or spam detection. We also show the potential for non-negativity to be helpful to non-binary problems by applying it to image classification.
Forward citations
Cited by 1 Pith paper
-
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection
Splitting public Yara rules into sub-signatures yields features that improve Windows malware detection on EMBER 2018 beyond the standard feature set.
Discussion (0). Continue with ORCID to comment.