arXiv AI
Jun 30
IBRSteG: Learning a Generalizable Steganography Framework for 3D Gaussian Splatting
arXiv:2606. 30024v1 Announce Type: cross Abstract: Recent advances in deep learning have notably improved steganographic message hiding.
X-SG$^2$S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks
The paper introduces X‑SG$^2$S, a feed‑forward framework that embeds 1D to 3D watermarks into 3D Gaussian Splatting (3DGS) scenes without altering the original rendering pipeline. It splits watermark messages into patches, uses a self‑adaptive gate to choose injection points, and an XD injection head to embed multi‑modal messages into sorted 3DGS points. A learnable gate and XD‑extraction heads recover the hidden messages, achieving robust watermarking with minimal interference to scene fidelity.
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arXiv AI
Aug 28
High-Fidelity Face Content Recovery via Tamper-Resilient Versatile Watermarking
The paper introduces VeriFi, a watermarking framework that protects face images from AI‑generated manipulation. It embeds a compact semantic latent watermark to preserve content, localizes pixel‑level edits without explicit payloads, and simulates realistic deepfake attacks to improve robustness. Experiments on CelebA‑HQ and FFHQ show that VeriFi outperforms existing methods in robustness, localization accuracy, and recovery quality.
arXiv AI
Aug 12
MarkNull: Model-Agnostic Watermark Removal in AI-Generated Images via On-Manifold Latent Manipulation
arXiv:2608. 10166v1 Announce Type: cross Abstract: Digital watermarking has emerged as a critical technique for provenance and copyright attribution in AI-generated imagery, yet its robustness against realistic, model-agnostic removal attacks remains poorly explored.
arXiv AI
2d ago
One Prompt Is Enough: Watermark Laundering Through Foundation Image Models
The paper introduces the concept of watermark laundering, where an attacker uses a single reconstruction prompt on public foundation image models to produce a visually faithful output that renders invisible watermarks undecodable. The authors evaluate this failure mode across six OpenAI and Google image editing models, three watermarking schemes, and 1,800 reconstructions, finding that OpenAI models cause the strongest payload disruption while Nano Banana 2 shows vulnerability of DwtDct under high-fidelity reconstruction. Prompt ablation experiments reveal that the disruption is driven by the reconstruction pathway itself rather than any specific removal instruction, highlighting prompt-conditioned reconstruction as a distinct attack interface.
arXiv Machine Learning
Aug 24
Keyed Provenance Watermarking with Complementary Lattice-Based Secure Aggregation for Federated Learning
arXiv:2608.20580v1 Announce Type: cross Abstract: Federated learning (FL) is vulnerable to multi-level attacks. However, existing methods address them separately, leaving FL exposed to data leakage,...
arXiv Computer Vision
Aug 28
KISS-GS: 3D Gaussian Splatting Compression Kept Simple
KISS-GS is a modular compression pipeline for 3D Gaussian Splatting (3DGS) scenes that separates compression from training. It first compacts a vanilla 3DGS scene by 15.7× using state‑of‑the‑art pruning, then encodes the result into the SOG‑XT image‑based format, achieving an additional 6.6× reduction. Optional encoding‑aware fine‑tuning can further cut the size by 2.2×, yielding total reductions of 85× to 319× on standard benchmarks while enabling web‑native decoding.
arXiv Computer Vision
3d ago
Asymmetric Phase Coding Video Watermarking
arXiv:2608.29212v1 Announce Type: cross Abstract: Existing video watermarking systems are symmetric: the party that can verify a mark holds the extractor weights or generator secret and can therefore...
arXiv AI
Jul 1
Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling
arXiv:2507. 11061v3 Announce Type: replace-cross Abstract: Recent advances in 3D neural representations and instance-level editing models have enabled the efficient creation of high-quality 3D content.
arXiv AI
Jun 16
Rel-Zero: Harnessing Patch-Pair Invariance for Robust Zero-Watermarking Against AI Editing
arXiv:2603. 17531v2 Announce Type: replace-cross Abstract: Recent advancements in diffusion-based image editing pose a significant threat to the authenticity of digital visual content.
arXiv Computer Vision
Aug 25
RobustGS: Unified Boosting of Feedforward 3D Gaussian Splatting under Low-Quality Conditions
arXiv:2508.03077v2 Announce Type: replace Abstract: Feedforward 3D Gaussian Splatting (3DGS) overcomes the limitations of optimization-based 3DGS by enabling fast and high-quality reconstruction with...
arXiv Computer Vision
Aug 27
IRIS: Visual-Semantic Binding for Forgery-Resistant Watermarking of Diffusion Images
IRIS is a training‑free watermarking scheme for diffusion‑generated images that binds a watermark to the image’s visual semantics. It derives an intrinsic ring identifier from a content code of the non‑watermarked image and injects it late in the generation trajectory, ensuring the mark survives common processing while breaking under semantic changes or foreign images. Experiments on three prompt datasets show IRIS reliably detects watermarks, maintains fidelity to the original image, and resists forgery techniques that defeat other marks.
Hugging Face Trending Papers
Jul 29
CASIAL: Geometric Distortion Robust Image Watermarking
Deep learning-based watermarking has shown strong robustness against non-geometric distortions, yet its performance under geometric transformations remains limited. Such transformations induce two fundamental failure modes: region removal, such as cropping or masking, which eliminates the information carried by removed pixels, and desynchronization, such as scaling or rotation, which misaligns pixel positions and disrupts decoding.