We lifted 8 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| mqraitem/self-gen-typo-attack | canonical | 4 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| encode_image | Ran | mqraitem/self-gen-typo-attack/utils_models/utils_gpt4.py pointer only (licence: NONE) · get_code("f41cb1a19b154297") |
| get_gpt4_data | Ran | mqraitem/self-gen-typo-attack/utils.py pointer only (licence: NONE) · get_code("95bcb0c3cf069e22") |
| get_gpt4_data_llm | Ran | mqraitem/self-gen-typo-attack/utils.py pointer only (licence: NONE) · get_code("b9d472b7f2267e67") |
| get_random_data | Ran | mqraitem/self-gen-typo-attack/utils.py pointer only (licence: NONE) · get_code("1c1ad9aaa08f3a38") |
| get_gpt4_llm_pred | Not yet run | mqraitem/self-gen-typo-attack/utils_models/utils_gpt4.py pointer only (licence: NONE) · get_code("fb9b530e93968dac") |
| get_gpt4_pred | Not yet run | mqraitem/self-gen-typo-attack/utils_models/utils_gpt4.py pointer only (licence: NONE) · get_code("e7615cd209323da1") |
| run_blip | Not yet run | mqraitem/self-gen-typo-attack/utils_models/utils_instructblip.py pointer only (licence: NONE) · get_code("2e08e894022db180") |
| run_llava | Not yet run | mqraitem/self-gen-typo-attack/utils_models/utils_llava.py pointer only (licence: NONE) · get_code("b607dd5d1f16b79b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Typographic attacks, adding misleading text to images, can deceive vision-language models (LVLMs). The susceptibility of recent large LVLMs like GPT4-V to such attacks is understudied, raising concerns about amplified misinformation in personal assistant applications. Previous attacks use simple strategies, such as random misleading words, which don't fully exploit LVLMs' language reasoning abilities. We introduce an experimental setup for testing typographic attacks on LVLMs and propose two novel self-generated attacks: (1) Class-based attacks, where the model identifies a similar class to deceive itself, and (2) Reasoned attacks, where an advanced LVLM suggests an attack combining a deceiving class and description. Our experiments show these attacks significantly reduce classification performance by up to 60\% and are effective across different models, including InstructBLIP and MiniGPT4. Code: https://github.com/mqraitem/Self-Gen-Typo-Attack
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.00626")
get_code_for_paper("2402.00626")
have("2402.00626")
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