REVIEW 6 cited by
Fine-Tuning Is All You Need to Mitigate Backdoor 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
read the original abstract
Backdoor attacks represent one of the major threats to machine learning models. Various efforts have been made to mitigate backdoors. However, existing defenses have become increasingly complex and often require high computational resources or may also jeopardize models' utility. In this work, we show that fine-tuning, one of the most common and easy-to-adopt machine learning training operations, can effectively remove backdoors from machine learning models while maintaining high model utility. Extensive experiments over three machine learning paradigms show that fine-tuning and our newly proposed super-fine-tuning achieve strong defense performance. Furthermore, we coin a new term, namely backdoor sequela, to measure the changes in model vulnerabilities to other attacks before and after the backdoor has been removed. Empirical evaluation shows that, compared to other defense methods, super-fine-tuning leaves limited backdoor sequela. We hope our results can help machine learning model owners better protect their models from backdoor threats. Also, it calls for the design of more advanced attacks in order to comprehensively assess machine learning models' backdoor vulnerabilities.
Forward citations
Cited by 6 Pith papers
-
Follow My Eyes: Backdoor Attacks on Goal-Directed Scanpath Prediction
Scene-conditioned spatial-misdirection and duration-inflation backdoors succeed at 2.5–10% poison ratios on multimodal scanpath predictors and resist five adapted defenses.
-
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation
BadSem shows that semantic mismatches between images and text can serve as stealthy backdoor triggers for VLMs, achieving near-perfect attack success with low poisoning rates.
-
Where Do Backdoors Live? A Component-Level Analysis of Backdoor Propagation in Speech Language Models
Backdoors propagate through SLM components with persistence or erasure depending on the targeted part, and poisoned samples are not directly separable from benign ones in shared multitask embeddings.
-
Mitigating Data Exfiltration Attacks through Layer-Wise Learning Rate Decay Fine-Tuning
A layer-wise learning rate decay fine-tuning protocol corrupts steganographically embedded training data in exported medical models while preserving classification utility.
-
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution
BadSR creates stealthy poisoned high-resolution labels for super-resolution backdoors, achieving above 80% attack success across five SR models while keeping labels visually close to clean images.
-
A Robust Attack: Displacement Backdoor Attack
Displacement Backdoor Attack blends shifted self-copies of an image into the original as a backdoor trigger and reportedly maintains high attack success under data augmentation.
Discussion (0). Continue with ORCID to comment.