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Wassim Bouaziz

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3 papers

2 author rows

ICLR Conference 2025 Conference Paper

Data Taggants: Dataset Ownership Verification Via Harmless Targeted Data Poisoning

Wassim Bouaziz
Nicolas Usunier
El-Mahdi El-Mhamdi

Dataset ownership verification, the process of determining if a dataset is used in a model's training data, is necessary for detecting unauthorized data usage and data contamination. Existing approaches, such as backdoor watermarking, rely on inducing a detectable behavior into the trained model on a part of the data distribution. However, these approaches have limitations, as they can be harmful to the model's performances or require unpractical access to the model's internals. Most importantly, previous approaches lack guarantee against false positives.\ This paper introduces *data taggants*, a novel non-backdoor dataset ownership verification technique. Our method uses pairs of out-of-distribution samples and random labels as secret *keys*, and leverages clean-label targeted data poisoning to subtly alter a dataset, so that models trained on it respond to the key samples with the corresponding key labels. The keys are built as to allow for statistical certificates with black-box access only to the model.\ We validate our approach through comprehensive and realistic experiments on ImageNet1k using ViT and ResNet models with state-of-the-art training recipes. Our findings demonstrate that data taggants can reliably detect models trained on the protected dataset with high confidence, without compromising validation accuracy, and show their superiority over backdoor watermarking. We demonstrate the stealthiness and robustness of our method % shows to be stealthy and robust against various defense mechanisms.

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TMLR Journal 2025 Journal Article

Inverting Gradient Attacks Makes Powerful Data Poisoning

Wassim Bouaziz
Nicolas Usunier
El-Mahdi El-Mhamdi

Gradient attacks and data poisoning tamper with the training of machine learning algorithms to maliciously alter them and have been proven to be equivalent in convex settings. The extent of harm these attacks can produce in non-convex settings is still to be determined. Gradient attacks are practical for fewer systems than data poisoning but have been argued to be more harmful since they can be arbitrary, whereas data poisoning reduces the attacker’s power to only being able to inject data points to training sets, via e.g. legitimate participation in a collaborative dataset. This raises the question whether the harm made by gradient attacks can be matched by data poisoning in non-convex settings. In this work, we provide a positive answer and show how data poisoning can mimic gradient attacks to perform an availability attack on (non-convex) neural networks. Through gradient inversion, commonly used to reconstruct data points from actual gradients, we show how reconstructing data points out of malicious gradients can be sufficient to perform a range of attacks. This allows us to show, for the first time, a worst-case availability attack on neural networks through data poisoning, degrading the model’s performances to random-level through a minority (as low as 1%) of poisoned points.

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NeurIPS Conference 2024 Conference Paper

Iteration Head: A Mechanistic Study of Chain-of-Thought

Vivien Cabannes
Charles Arnal
Wassim Bouaziz
Alice Yang
Francois Charton
Julia Kempe

Chain-of-Thought (CoT) reasoning is known to improve Large Language Models both empirically and in terms of theoretical approximation power. However, our understanding of the inner workings and conditions of apparition of CoT capabilities remains limited. This paper helps fill this gap by demonstrating how CoT reasoning emerges in transformers in a controlled and interpretable setting. In particular, we observe the appearance of a specialized attention mechanism dedicated to iterative reasoning, which we coined "iteration heads". We track both the emergence and the precise working of these iteration heads down to the attention level, and measure the transferability of the CoT skills to which they give rise between tasks.

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