arXiv subjects

Nicole Meng

Publications and source records attributed to Nicole Meng.

6 recordsLinked to original sources

arXiv2026

SHield: Watermarking 4D Gaussian Splatting via Spherical Harmonic Coefficient Perturbation

4D Gaussian Splatting (4DGS) models are computationally expensive to train but distributed as easily copied parameter files. Existing watermarks protect these assets by fine-tuning the scene against a neural decoder to extract bit strings from rendered video, a heavy process vulnerable to the temporal dynamics of dynamic scenes. However, if the threat model targets the redistributed file itself, verification can be vastly simplified. Because the canonical Spherical-Harmonic (SH) coefficients of 4DGS are time- and view-invariant, parameter-space perturbations can be recovered via deterministic arithmetic. We propose **SHield**, the first non-blind, decoder-free watermarking approach that natively embeds full-resolution RGB images into 4DGS files. SHield tiles a zero-mean payload across the SH coefficients of a key-selected Gaussian subset, applying a brief anchored optimization to preserve visual fidelity. To verify ownership, it geometrically re-associates the key using canonical Gaussian centres to survive file tampering and recovers the payload in closed form. Across 30 scenes (D-NeRF, HyperNeRF, DyNeRF), SHield guarantees near-perfect payload recovery (Pearson $r \geq 0.991$) on all 150 tested models with a mean rendering penalty of $\leq 0.13$~dB. Held-out evaluation yields perfect detection on 400 true trials and zero false positives across $1{,}600$ wrong-subset null trials. We also strictly bound the method's robustness: SHield survives INT8 quantization and $30\%$ opacity pruning for the image payload, and on aggressive SH-degree-1 truncation bit payloads are still decodable.

cs.CV↗

arXiv2026

Spoofing the Latent State: Imperceptible Goal-Image Attacks Derail Planning in LeWorldModel

Joint-embedding predictive architectures (JEPAs) have recently emerged as general-purpose visual world models for control. A prominent example is LeWorldModel (LeWM), which trains end-to-end from pixels using an isotropic Gaussian anti-collapse regularizer (SIGReg) and plans by minimizing the squared Euclidean distance between predicted and goal embeddings. In this work, we investigate the test-time robustness of this latent planning interface. Analyzing the learned metric on Push-T, we first find that task-relevant object state heavily dominates latent distance: shifting the block by a single pixel displaces the embedding $3\times$ further than an equivalent agent shift, and swapping the block pose alone accounts for $99\%$ of the distance between scene pairs. However, because this pose information is distributed across a curved, high-dimensional manifold rather than a fixed linear subspace, the encoder cannot be trivially edited post-hoc to isolate or redirect it. We next demonstrate that this geometry is highly vulnerable at test time. A white-box $\ell_\infty$ perturbation of just $ε{=}4/255$ applied to a single input frame causes the encoder to register the block at an arbitrary, attacker-chosen pose in $73$--$95\%$ of scenes without disturbing the encoded agent state. Perturbing either the goal image or the live camera observation drops Push-T planning success from $87\%$ to $32\%$, whereas uniform random noise at twice the magnitude has negligible impact. This vulnerability persists across all four standard LeWM benchmarks (Push-T, Two-Room, Reacher, and OGBench-Cube). At $ε{=}8/255$, the attack bridges $82$--$97\%$ of the squared latent distance to the target embedding in every environment, inducing planning degradation proportional to each task's reliance on the spoofed entity.

cs.CV↗

arXiv2026

CLDRoute: Conditional Latent Diffusion for Routability Map Generation in Physical Design

Accurate routability estimation during physical design is important for reducing costly post-routing iterations. Prior learning-based methods treat this task as deterministic prediction, mapping placement-stage features to a single congestion or DRC outcome. We instead formulate routability estimation as a conditional generation problem, where both routing congestion and DRC violations are modeled as spatially structured routability fields. Our framework, Conditional Latent Diffusion for Routeability estimation (CLDRoute), uses physics-aware conditioning and task-specific latent modeling to handle the different characteristics of congestion and DRC maps. This allows our method to supports sample-based inference, producing both a mean prediction and a spatial uncertainty estimate for the same input design. On CircuitNet 2.0 (N28), our method achieves, for DRC violation generation, an SSIM of 0.9678, an MAE of 0.0028, and a TopK@1% of 0.3494; for congestion generation, it achieves an SSIM of 0.9031, an MAE of 0.0286, and an NZ-Pearson of 0.3692. Overall, our framework provides a more practical view of routability at placement by generating both the expected outcome and its uncertainty.

cs.LG↗

arXiv2026

3DEditSafe: Defending 3D Editing Pipelines from Unsafe Generation

Recent advances in 3D generative editing, particularly pipelines based on 3D Gaussian Splatting (3DGS), have achieved high-fidelity, multi-view-consistent scene manipulation from text prompts. However, we find that these pipelines also introduce new safety risks when unsafe prompts produce edits that are propagated and optimized across views. In this work, we study unsafe generation in 3D editing pipelines and show that such behavior can lead to coherent, undesirable Not-Safe-For-Work (NSFW) content in the final 3D representation. To address this, we propose 3DEditSafe, a safety-regularized 3D editing framework that constrains unsafe semantic propagation during optimization. 3DEditSafe combines generation-stage safety guidance with rendered-view 3D safety regularization, safe semantic projection, residue suppression, and mask-aware preservation to steer optimization away from unsafe editing directions. We evaluate our approach on EditSplat scenes using an object-compatible unsafe prompt benchmark and show that 2D safety guidance alone is not consistently sufficient to prevent unsafe 3D edits. 3DEditSafe reduces unsafe semantic alignment and view-level attack success rates, while revealing a safety-quality tradeoff in which stronger unsafe suppression can introduce artifacts or reduce unsafe-prompt fidelity. To our knowledge, this work is the first attempt to study and defend against unsafe generation in text-driven 3D editing pipelines, highlighting the need for safety mechanisms that operate directly on optimized 3D representations.

cs.GR↗

arXiv2026

GIF: A Conditional Multimodal Generative Framework for IR Drop Imaging in Chip Layouts

IR drop analysis is essential in physical chip design to ensure the power integrity of on-chip power delivery networks. Traditional Electronic Design Automation (EDA) tools have become slow and expensive as transistor density scales. Recent works have introduced machine learning (ML)-based methods that formulate IR drop analysis as an image prediction problem. These existing ML approaches fail to capture both local and long-range dependencies and ignore crucial geometrical and topological information from physical layouts and logical connectivity. To address these limitations, we propose GIF, a Generative IR drop Framework that uses both geometrical and topological information to generate IR drop images. GIF fuses image and graph features to guide a conditional diffusion process, producing high-quality IR drop images. For instance, On the CircuitNet-N28 dataset, GIF achieves 0.78 SSIM, 0.95 Pearson correlation, 21.77 PSNR, and 0.026 NMAE, outperforming prior methods. These results demonstrate that our framework, using diffusion based multimodal conditioning, reliably generates high quality IR drop images. This shows that IR drop analysis can effectively leverage recent advances in generative modeling when geometric layout features and logical circuit topology are jointly modeled. By combining geometry aware spatial features with logical graph representations, GIF enables IR drop analysis to benefit from recent advances in generative modeling for structured image generation.

cs.CV↗

arXiv2024

Theoretical Corrections and the Leveraging of Reinforcement Learning to Enhance Triangle Attack

Adversarial examples represent a serious issue for the application of machine learning models in many sensitive domains. For generating adversarial examples, decision based black-box attacks are one of the most practical techniques as they only require query access to the model. One of the most recently proposed state-of-the-art decision based black-box attacks is Triangle Attack (TA). In this paper, we offer a high-level description of TA and explain potential theoretical limitations. We then propose a new decision based black-box attack, Triangle Attack with Reinforcement Learning (TARL). Our new attack addresses the limits of TA by leveraging reinforcement learning. This creates an attack that can achieve similar, if not better, attack accuracy than TA with half as many queries on state-of-the-art classifiers and defenses across ImageNet and CIFAR-10.

cs.LG↗