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Found 4 papers, 2 papers with code
EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methods
From both the human study and automated evaluation, we find that: (i) Instruct-Pix2Pix, Null-Text and SINE are the top-performing methods averaged across different edit types, however {\it only} Instruct-Pix2Pix and Null-Text are able to preserve original image properties; (ii) Most of the editing methods fail at edits involving spatial operations (e. g., changing the position of an object).
PRIME: Prioritizing Interpretability in Failure Mode Extraction
To improve on these shortcomings, we propose a novel approach that prioritizes interpretability in this problem: we start by obtaining human-understandable concepts (tags) of images in the dataset and then analyze the model's behavior based on the presence or absence of combinations of these tags.
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
Moreover, we show that watermarking methods are vulnerable to spoofing attacks where the attacker aims to have real images identified as watermarked ones, damaging the reputation of the developers.
ZeroGrad : Mitigating and Explaining Catastrophic Overfitting in FGSM Adversarial Training
Making deep neural networks robust to small adversarial noises has recently been sought in many applications.
