Geodesic
Realistic adversarial attacks on object detectors using generative models
Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part II–2, Tome 530 (2023), pp. 128-140

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An important limitation of existing adversarial attacks on real-world object detectors lies in their threat model: adversarial patch-based methods often produce suspicious images while image generation approaches do not restrict the attacker's capabilities of modifying the original scene. We design a threat model where the attacker modifies individual image segments and is required to produce realistic images. We also develop and evaluate a white-box attack that utilizes generative adversarial nets and diffusion models as a generator of malicious images. Our attack is able to produce high-fidelity images as measured by the Fréchet inception distance (FID) and reduces the mAP of Faster R-CNN model by > 0.2 on Cityscapes and COCO-Stuff datasets. A PyTorch implementation of our attack is available at https://github.com/DariaShel/gan-attack. 
@article{ZNSL_2023_530_a9,
     author = {D. Shelepneva and K. Arkhipenko},
     title = {Realistic adversarial attacks on object detectors using generative models},
     journal = {Zapiski Nauchnykh Seminarov POMI},
     pages = {128--140},
     publisher = {mathdoc},
     volume = {530},
     year = {2023},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/ZNSL_2023_530_a9/}
}
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D. Shelepneva; K. Arkhipenko. Realistic adversarial attacks on object detectors using generative models. Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part II–2, Tome 530 (2023), pp. 128-140. http://geodesic.mathdoc.fr/item/ZNSL_2023_530_a9/