Author name cluster

Tuan Tran

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.

4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Transferable Black-Box One-Shot Forging of Watermarks via Image Preference Models

Tomáš Souček
Sylvestre-Alvise Rebuffi
Pierre Fernandez
Nikola Jovanović
Hady Elsahar
Valeriu Lacatusu
Tuan Tran
Alexandre Mourachko

Recent years have seen a surge in interest in digital content watermarking techniques, driven by the proliferation of generative models and increased legal pressure. With an ever-growing percentage of AI-generated content available online, watermarking plays an increasingly important role in ensuring content authenticity and attribution at scale. There have been many works assessing the robustness of watermarking to removal attacks, yet, watermark forging, the scenario when a watermark is stolen from genuine content and applied to malicious content, remains underexplored. In this work, we investigate watermark forging in the context of widely used post-hoc image watermarking. Our contributions are as follows. First, we introduce a preference model to assess whether an image is watermarked. The model is trained using a ranking loss on purely procedurally generated images without any need for real watermarks. Second, we demonstrate the model's capability to remove and forge watermarks by optimizing the input image through backpropagation. This technique requires only a single watermarked image and works without knowledge of the watermarking model, making our attack much simpler and more practical than attacks introduced in related work. Third, we evaluate our proposed method on a variety of post-hoc image watermarking models, demonstrating that our approach can effectively forge watermarks, questioning the security of current watermarking approaches. Our code and further resources are publicly available.

PDF Details

ICML Conference 2024 Conference Paper

Proactive Detection of Voice Cloning with Localized Watermarking

Robin San Roman
Pierre Fernandez
Hady Elsahar
Alexandre Défossez
Teddy Furon
Tuan Tran

In the rapidly evolving field of speech generative models, there is a pressing need to ensure audio authenticity against the risks of voice cloning. We present AudioSeal, the first audio watermarking technique designed specifically for localized detection of AI-generated speech. AudioSeal employs a generator / detector architecture trained jointly with a localization loss to enable localized watermark detection up to the sample level, and a novel perceptual loss inspired by auditory masking, that enables AudioSeal to achieve better imperceptibility. AudioSeal achieves state-of-the-art performance in terms of robustness to real life audio manipulations and imperceptibility based on automatic and human evaluation metrics. Additionally, AudioSeal is designed with a fast, single-pass detector, that significantly surpasses existing models in speed, achieving detection up to two orders of magnitude faster, making it ideal for large-scale and real-time applications. Code is available at https: //github. com/facebookresearch/audioseal

Details

ICRA Conference 2021 Conference Paper

Feasible and Adaptive Multimodal Trajectory Prediction with Semantic Maneuver Fusion

Hendrik Berkemeyer
Riccardo Franceschini
Tuan Tran
Lin Che 0002
Gordon Pipa

Predicting trajectories of participating vehicles is a crucial task towards full and safe autonomous driving. General unconstrained machine learning methods often report unrealistic predictions, and need to be combined with different motion constraints. Existing work either defines some shallow maneuvers and modes to regulate the output, or uses vehicle dynamics as the main source of constraints, for instance via kinematic models. In contrast, we present a new approach that guides the learning models by complex semantic maneuvers, constructing from both vehicle states and the surrounding objects. We propose a novel Maneuver Fusion layer to incorporate the logic-based semantic maneuvers into deep neural networks. We also incorporate and refine the different loss functions to account for the feasibility of the trajectories, adapting to different maneuver types. Finally, we design a hierarchical multi-task learning framework with adaptive loss to provide a multimodal trajectory prediction. Our method was evaluated on a large-scale real world data set for urban driving and was shown to give promising improvement over the states of the art.

Details

IROS Conference 2021 Conference Paper

Identifying Valid Robot Configurations via a Deep Learning Approach

Tuan Tran
Chinwe Ekenna

Many state-of-art robotics applications require fast and efficient motion planning algorithms. Existing motion planning methods become less effective as the dimensionality of the robot and its workspace increases, especially the computational cost of collision detection routines. In this work, we present a framework to address the cost of expensive primitive operations in sampling-based motion planning. This framework determines the validity of a sample robot configuration through a novel combination of a Contractive AutoEncoder (CAE), which captures an occupancy grids representation of the robot's workspace, and a Multilayer Perceptron (MLP), which efficiently predicts the collision state of the robot using the output from the CAE. We evaluate our framework on multiple planning problems with a variety of robots in 2D and 3D workspaces. The results show that (1) the framework is computationally efficient in all investigated problems, and (2) the framework generalizes well to new workspaces.

Details