arXiv subjects

Yantian Shen

Publications and source records attributed to Yantian Shen.

5 recordsLinked to original sources

arXiv2026

GuidedRay: Diversity-Guided Direction Discovery for Targeted Hard-Label Black-Box Attacks

Deep neural networks are vulnerable to adversarial attacks. Among black-box attacks, targeted decision-based attacks are particularly difficult: the attacker observes only the target model's top-1 label and aims to make it predict a prespecified target class under a bounded perturbation. Before perturbation refinement, the attacker must discover a direction that reaches the prescribed target region. This initialization step can incur substantial query cost. We propose GuidedRay, a targeted decision-based attack based on diversity-guided direction discovery. GuidedRay builds on two observations: target-class reference samples provide useful target-conditioned direction priors, and diverse candidates increase the probability of discovering a targeted adversarial direction. GuidedRay generates varied candidates from one or multiple target-class references and uses a one-query Fast Test to screen their induced sign directions. Once a feasible direction is found, GuidedRay applies Ray Search to reduce its decision-boundary radius. Experiments on CIFAR-10, CIFAR-100, and ImageNet demonstrate that GuidedRay consistently outperforms five state-of-the-art decision-based attacks at four evaluated query budgets from 500 to 5,000, with particularly pronounced gains in direction discovery during initialization. Against models protected by adversarial training or TRADES, it likewise achieves the highest attack success rate at all four query budgets.

cs.CR↗

arXiv2026

Cryptanalytic Extraction of Neural Networks Without Known Architecture Assumption

Cryptanalytic model extraction aims to reconstruct a functionally equivalent model through black-box interactions with the victim model. Under the fundamental assumption that the network architecture is completely known, existing attacks achieve the goal by recovering the model parameters. In this paper, we explore whether this assumption can be removed practically. Focusing on ReLU fully connected networks, which are widely studied in this field, we propose a guess-and-determine framework that jointly recovers the network architecture (including network depth and hidden-layer dimensions) and the model parameters. This framework is based on a simple yet effective high-level idea: after designing a parameter recovery attack under the known-architecture assumption, we can analyze the architecture-sensitive traces observed during parameter recovery to recover the network architecture. We identify two such traces in differential extraction attacks: (i) a \emph{zero suffix} in the merged weight vectors produced by signature recovery, whose length reveals the hidden layer dimension; and (ii) an \emph{equality pattern} in the preimage-based sign recovery, which occurs only under the true hidden layer dimension. These two signals give rise to two routes for network architecture recovery. For the second-to-last layer, we further propose two methods, one for identifying it, and one for recovering its dimension. Practical end-to-end attacks are implemented on a wide range of ReLU neural networks, including both expansive and non-expansive networks. To the best of our knowledge, this is the first time the feasibility of achieving functionally equivalent extraction on deep neural networks, after removing the known-architecture assumption, has been demonstrated in practice.

cs.CR↗

arXiv2026

See-and-Reach: Precise Vision-Language Navigation for UAVs within the Field of View

UAV Vision-Language Navigation (UAV-VLN) is typically formulated as a holistic search-and-reach problem, where long-range target discovery and final target approach are optimized and evaluated jointly. This formulation makes it difficult to assess a critical capability of aerial embodied agents, namely whether a UAV can accurately ground a visible target and translate vision-language evidence into precise 3D motion once the target enters its field of view. To address this limitation, we introduce UAV-VLN-FOV, a target-visible navigation task that isolates the see-and-reach stage and enables a more diagnostic evaluation of terminal reaching ability. We further propose 3DG-VLN, a vision-language waypoint prediction framework guided by dynamic 3D direction cues to enhance fine-grained visual grounding and spatial direction alignment for precise target reaching. Specifically, 3DG-VLN adaptively processes high-resolution front-view and downward-view observations to preserve fine-grained visual and geometric details for target grounding. It also updates the target-relative direction online during closed-loop navigation, allowing the agent to maintain spatial alignment with the target and reduce accumulated direction drift. To support this task, we construct a dedicated high-resolution benchmark which contains 2,717 trajectories with target-oriented high-level instructions, high-resolution front-view and downward-view egocentric observations, and continuous 3D waypoint annotations. Experiments show that 3DG-VLN outperforms competitive UAV-VLN baselines, achieving a 13.82\% improvement in success rate. Real-world trials further demonstrate the potential of 3DG-VLN for practical see-and-reach navigation. The source code and benchmark are available at https://github.com/xuefanfu/3DG-VLN.

cs.CV↗

arXiv2025

Delving into Cryptanalytic Extraction of PReLU Neural Networks

The machine learning problem of model extraction was first introduced in 1991 and gained prominence as a cryptanalytic challenge starting with Crypto 2020. For over three decades, research in this field has primarily focused on ReLU-based neural networks. In this work, we take the first step towards the cryptanalytic extraction of PReLU neural networks, which employ more complex nonlinear activation functions than their ReLU counterparts. We propose a raw output-based parameter recovery attack for PReLU networks and extend it to more restrictive scenarios where only the top-m probability scores are accessible. Our attacks are rigorously evaluated through end-to-end experiments on diverse PReLU neural networks, including models trained on the MNIST dataset. To the best of our knowledge, this is the first practical demonstration of PReLU neural network extraction across three distinct attack scenarios.

cs.CR↗

arXiv2024

Hard-Label Cryptanalytic Extraction of Neural Network Models

The machine learning problem of extracting neural network parameters has been proposed for nearly three decades. Functionally equivalent extraction is a crucial goal for research on this problem. When the adversary has access to the raw output of neural networks, various attacks, including those presented at CRYPTO 2020 and EUROCRYPT 2024, have successfully achieved this goal. However, this goal is not achieved when neural networks operate under a hard-label setting where the raw output is inaccessible. In this paper, we propose the first attack that theoretically achieves functionally equivalent extraction under the hard-label setting, which applies to ReLU neural networks. The effectiveness of our attack is validated through practical experiments on a wide range of ReLU neural networks, including neural networks trained on two real benchmarking datasets (MNIST, CIFAR10) widely used in computer vision. For a neural network consisting of $10^5$ parameters, our attack only requires several hours on a single core.

cs.CR↗