{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/defense-prefix-for-preventing-typographic","title":"Defense-Prefix for Preventing Typographic Attacks on CLIP","arxiv_id":"2304.04512","date":"2023-04-10","proceeding":null,"authors":["Hiroki Azuma","Yusuke Matsui"],"abstract":"Vision-language pre-training models (VLPs) have exhibited revolutionary improvements in various vision-language tasks. In VLP, some adversarial attacks fool a model into false or absurd classifications. Previous studies addressed these attacks by fine-tuning the model or changing its architecture. However, these methods risk losing the original model's performance and are difficult to apply to downstream tasks. In particular, their applicability to other tasks has not been considered. In this study, we addressed the reduction of the impact of typographic attacks on CLIP without changing the model parameters. To achieve this, we expand the idea of \"prefix learning\" and introduce our simple yet effective method: Defense-Prefix (DP), which inserts the DP token before a class name to make words \"robust\" against typographic attacks. Our method can be easily applied to downstream tasks, such as object detection, because the proposed method is independent of the model parameters. Our method significantly improves the accuracy of classification tasks for typographic attack datasets, while maintaining the zero-shot capabilities of the model. In addition, we leverage our proposed method for object detection, demonstrating its high applicability and effectiveness. The codes and datasets are available at https://github.com/azuma164/Defense-Prefix.","url_abs":"https://arxiv.org/abs/2304.04512v3","url_pdf":"https://arxiv.org/pdf/2304.04512v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"defense-prefix-for-preventing-typographic","repo_url":"https://github.com/azuma164/defense-prefix","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"clip","method_name":"CLIP"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2304.04512","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04512"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/azuma164/defense-prefix","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"241a2a00ba0c307b","entry":"pretify_classname","repo":"azuma164/defense-prefix","repo_kind":"official","path":"datasets/eurosat.py","file_url":"https://github.com/azuma164/defense-prefix/blob/HEAD/datasets/eurosat.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"241a2a00ba0c307b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}