Author name cluster

Alaa Khaddaj

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.

3 papers

1 author row

ICLR Conference 2025 Conference Paper

Small-to-Large Generalization: Training Data Influences Models Consistently Across Scale

Alaa Khaddaj
Logan Engstrom
Aleksander Madry

Choice of training data distribution greatly influences model behavior. Yet, in large-scale settings, precisely characterizing *how* changes in training data affects predictions is often difficult due to model training costs. Current practice is to instead extrapolate from scaled down, inexpensive-to-train proxy models. However, changes in data do not influence smaller and larger models identically. Therefore, understanding how choice of data affects large-scale models raises the question: how does training data distribution influence model behavior across compute scale? We find that small- and large-scale language model predictions (generally) *do* highly correlate across choice of training data. Equipped with these findings, we characterize how proxy scale affects effectiveness in two downstream proxy model applications: data attribution and dataset selection.

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ICML Conference 2023 Conference Paper

Raising the Cost of Malicious AI-Powered Image Editing

Hadi Salman
Alaa Khaddaj
Guillaume Leclerc
Andrew Ilyas
Aleksander Madry

We present an approach to mitigating the risks of malicious image editing posed by large diffusion models. The key idea is to immunize images so as to make them resistant to manipulation by these models. This immunization relies on injection of imperceptible adversarial perturbations designed to disrupt the operation of the targeted diffusion models, forcing them to generate unrealistic images. We provide two methods for crafting such perturbations, and then demonstrate their efficacy. Finally, we discuss a policy component necessary to make our approach fully effective and practical—one that involves the organizations developing diffusion models, rather than individual users, to implement (and support) the immunization process.

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ICML Conference 2023 Conference Paper

Rethinking Backdoor Attacks

Alaa Khaddaj
Guillaume Leclerc
Aleksandar Makelov
Kristian Georgiev
Hadi Salman
Andrew Ilyas
Aleksander Madry

In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against such attacks involves viewing inserted examples as outliers in the training set and using techniques from robust statistics to detect and remove them. In this work, we present a different approach to the backdoor attack problem. Specifically, we show that without structural information about the training data distribution, backdoor attacks are indistinguishable from naturally-occuring features in the data—and thus impossible to "detect" in a general sense. Then, guided by this observation, we revisit existing defenses against backdoor attacks and characterize the (often latent) assumptions they make, and on which they depend. Finally, we explore an alternative perspective on backdoor attacks: one that assumes these attacks correspond to the strongest feature in the training data. Under this assumption (which we make formal) we develop a new primitive for detecting backdoor attacks. Our primitive naturally gives rise to a detection algorithm that comes with theoretical guarantees, and is effective in practice.

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