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08/22/2023
Ceci n'est pas une pomme: Adversarial Illusions in Multi-Modal Embeddings
Multi-modal encoders map images, sounds, texts, videos, etc. into a sing...
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07/19/2023
(Ab)using Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs
We demonstrate how images and sounds can be used for indirect prompt and...
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02/09/2023
Hyperparameter Search Is All You Need For Training-Agnostic Backdoor Robustness
Commoditization and broad adoption of machine learning (ML) technologies...
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03/15/2022
Training a Tokenizer for Free with Private Federated Learning
Federated learning with differential privacy, i.e. private federated lea...
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12/09/2021
Spinning Language Models for Propaganda-As-A-Service
We investigate a new threat to neural sequence-to-sequence (seq2seq) mod...
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11/03/2021
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation
We design a scalable algorithm to privately generate location heatmaps o...
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07/22/2021
Spinning Sequence-to-Sequence Models with Meta-Backdoors
We investigate a new threat to neural sequence-to-sequence (seq2seq) mod...
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05/08/2020
Blind Backdoors in Deep Learning Models
We investigate a new method for injecting backdoors into machine learnin...
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03/14/2020
Decentralized Policy-Based Private Analytics
We are increasingly surrounded by applications, connected devices, servi...
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02/12/2020
Salvaging Federated Learning by Local Adaptation
Federated learning (FL) is a heavily promoted approach for training ML m...
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05/28/2019
Differential Privacy Has Disparate Impact on Model Accuracy
Differential privacy (DP) is a popular mechanism for training machine le...
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07/02/2018
