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Robert Hönig

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

ICLR Conference 2025 Conference Paper

Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI

Robert Hönig
Javier Rando
Nicholas Carlini
Florian Tramèr

Artists are increasingly concerned about advancements in image generation models that can closely replicate their unique artistic styles. In response, several protection tools against style mimicry have been developed that incorporate small adversarial perturbations into artworks published online. In this work, we evaluate the effectiveness of popular protections---with millions of downloads---and show they only provide a false sense of security. We find that low-effort and "off-the-shelf" techniques, such as image upscaling, are sufficient to create robust mimicry methods that significantly degrade existing protections. Through a user study, we demonstrate that **all existing protections can be easily bypassed**, leaving artists vulnerable to style mimicry. We caution that tools based on adversarial perturbations cannot reliably protect artists from the misuse of generative AI, and urge the development of alternative protective solutions.

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ICML Conference 2022 Conference Paper

DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning

Robert Hönig
Yiren Zhao
Robert D. Mullins

Federated Learning (FL) is a powerful technique to train a model on a server with data from several clients in a privacy-preserving manner. FL incurs significant communication costs because it repeatedly transmits the model between the server and clients. Recently proposed algorithms quantize the model parameters to efficiently compress FL communication. We find that dynamic adaptations of the quantization level can boost compression without sacrificing model quality. We introduce DAdaQuant as a doubly-adaptive quantization algorithm that dynamically changes the quantization level across time and different clients. Our experiments show that DAdaQuant consistently improves client$\rightarrow$server compression, outperforming the strongest non-adaptive baselines by up to $2. 8\times$.

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