Lite-BD: A Lightweight Black-box Backdoor Defense via Reviving Multi-Stage Image Transformations
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911551959597056 |
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| author | Miah, Abdullah Arafat Bi, Yu |
| author_facet | Miah, Abdullah Arafat Bi, Yu |
| contents | Deep Neural Networks (DNNs) are vulnerable to backdoor attacks. Due to the nature of Machine Learning as a Service (MLaaS) applications, black-box defenses are more practical than white-box methods, yet existing purification techniques suffer from key limitations: a lack of justification for specific transformations, dataset dependency, high computational overhead, and a neglect of frequency-domain transformations. This paper conducts a preliminary study on various image transformations, identifying down-upscaling as the most effective backdoor trigger disruption technique. We subsequently propose \texttt{Lite-BD}, a lightweight two-stage blackbox backdoor defense. \texttt{Lite-BD} first employs a super-resolution-based down-upscaling stage to neutralize spatial triggers. A secondary stage utilizes query-based band-by-band frequency filtering to remove triggers hidden in specific bands. Extensive experiments against state-of-the-art attacks demonstrate that \texttt{Lite-BD} provides robust and efficient protection. Codes can be found at https://github.com/SiSL-URI/Lite-BD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07197 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Lite-BD: A Lightweight Black-box Backdoor Defense via Reviving Multi-Stage Image Transformations Miah, Abdullah Arafat Bi, Yu Cryptography and Security Deep Neural Networks (DNNs) are vulnerable to backdoor attacks. Due to the nature of Machine Learning as a Service (MLaaS) applications, black-box defenses are more practical than white-box methods, yet existing purification techniques suffer from key limitations: a lack of justification for specific transformations, dataset dependency, high computational overhead, and a neglect of frequency-domain transformations. This paper conducts a preliminary study on various image transformations, identifying down-upscaling as the most effective backdoor trigger disruption technique. We subsequently propose \texttt{Lite-BD}, a lightweight two-stage blackbox backdoor defense. \texttt{Lite-BD} first employs a super-resolution-based down-upscaling stage to neutralize spatial triggers. A secondary stage utilizes query-based band-by-band frequency filtering to remove triggers hidden in specific bands. Extensive experiments against state-of-the-art attacks demonstrate that \texttt{Lite-BD} provides robust and efficient protection. Codes can be found at https://github.com/SiSL-URI/Lite-BD. |
| title | Lite-BD: A Lightweight Black-box Backdoor Defense via Reviving Multi-Stage Image Transformations |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2602.07197 |