Datasets:

ysjung77
/

FlipDir

license: cc-by-4.0
task_categories:
  - visual-question-answering
tags:
  - vlm
  - robustness
  - answer-flip
  - benchmark
pretty_name: FlipDir Answer-Flip Testbeds

FlipDir — Answer-Flip Testbeds under Benign Visual Variations

Model-conditioned testbeds of answer flips: cases where a vision–language model changes its final answer under a benign visual variation (exposure, white balance, JPEG, blur, small rotation) that a human would treat as inconsequential. Companion data for the FlipDir defense — code at https://github.com/YeonsungJung/FlipDir.

What is (and isn't) here

Each testbed ships the metadata + seeds + variation parameters + flip labels, not the rendered images. Because generation is deterministic (batch-1 greedy, fixed seed chain), the varied images regenerate byte-for-byte from the original source image + the stored seed/params using the released code. This keeps the release small and avoids redistributing the source datasets' images.

Regenerate images:

# in the code repo, with the same seed
bash run_ours.sh <qwen8b|gemma12b> <attack> gen

Layout

paper/<model>/<testbed>/<split>/meta/*.json     # settings used in the paper
etc/<model>/<testbed>/<split>/meta/*.json        # extra intensity/seed variants
  <model>   = qwen3vl-8b | gemma-3-12b
  <testbed> = exposure_shift | wb_shift | jpeg_double            (M3CoT, science)
              exposure_shift_robo | brightness_shift_robo | motion_shake_robo (Robo2VLM)
              window_level | medical_blur | detector_rotation    (GMAI-MMBench, medical)
  <split>   = train | test

A flip label is defined relative to the model's own clean-image answer, so every testbed is built per model. Current release: Gemma-3-12B all 9 testbeds; Qwen3-VL-8B 7 of 9 (robotics exposure_shift_robo / brightness_shift_robo are being regenerated).

Metadata schema (per parent question)

id, split, category_rank, attack, text_attack, image_attack_intensity, model, seed, gt, prompt, orig_img_path, orig_raw, orig_norm, orig_correct, raw_counts, flips, nonflips, limits, gen_cfg — orig_norm is the clean-image answer, flips/nonflips list the varied children and their (raw, normalized) outputs, seed+image_attack_intensity reproduce the variation, orig_img_path points into the source dataset.

Source datasets (obtain images from the originals)

M3CoT — science reasoning · https://huggingface.co/datasets/LightChen2333/M3CoT
Robo2VLM — robot-scene VQA · https://huggingface.co/datasets/keplerc/Robo2VLM
GMAI-MMBench — medical VQA · https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench

Please follow each source dataset's license for the underlying images.

Citation

FlipDir (preprint). Code and paper: https://github.com/YeonsungJung/FlipDir