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Found 18 papers, 9 papers with code
ErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models
Moreover, ErasableMask also exhibits outstanding perturbation erasion performance, achieving over 90% erasion success rate.
Three-in-One: Robust Enhanced Universal Transferable Anti-Facial Retrieval in Online Social Networks
Deep hash models are easily influenced by adversarial examples, which can be leveraged to protect private images from malicious retrieval.
Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection
Compared to state-of-the-art white-box and black-box attacks, R$^2$BA shows significant improvements of 15\%--72\% and 21\%--47\% in anti-detection performance under the original and robust scenario respectively, offering valuable insights for the security of AIGC detection in real-world applications.
DN-4DGS: Denoised Deformable Network with Temporal-Spatial Aggregation for Dynamic Scene Rendering
Dynamic scenes rendering is an intriguing yet challenging problem.
MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting
Specifically, we first introduce an optical flow decoupling module that decouples optical flow into camera flow and motion flow, corresponding to camera movement and object motion respectively.
TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN
With the development of artificial intelligence, neural networks play a key role in network intrusion detection systems (NIDS).
Plane2Depth: Hierarchical Adaptive Plane Guidance for Monocular Depth Estimation
Specifically, in the proposed plane guided depth generator (PGDG), we design a set of plane queries as prototypes to softly model planes in the scene and predict per-pixel plane coefficients.
Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection
3D object detection is essential for understanding 3D scenes.
Pamba: Enhancing Global Interaction in Point Clouds via State Space Model
Transformers have demonstrated impressive results for 3D point cloud semantic segmentation.
DIP-Watermark: A Double Identity Protection Method Based on Robust Adversarial Watermark
This strategy enhances the representation of universal carrier features, mitigating multi-objective optimization conflicts in watermarking.
Unsupervised Template-assisted Point Cloud Shape Correspondence Network
Unsupervised point cloud shape correspondence aims to establish point-wise correspondences between source and target point clouds.
BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation
To generate higher quality pseudo-labels and achieve more precise weakly supervised 3DIS results, we propose the Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation (BSNet), which devises a novel pseudo-labeler called Simulation-assisted Transformer.
AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies
With the continuous improvements of deepfake methods, forgery messages have transitioned from single-modality to multi-modal fusion, posing new challenges for existing forgery detection algorithms.
Dual Defense: Adversarial, Traceable, and Invisible Robust Watermarking against Face Swapping
Dual Defense invisibly embeds a single robust watermark within the target face to actively respond to sudden cases of malicious face swapping.
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes
Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics.
Ranked #2 on
Unsupervised Monocular Depth Estimation
on KITTI-C
(using extra training data)
Universal Defensive Underpainting Patch: Making Your Text Invisible to Optical Character Recognition
In this work, we propose a novel and effective defense mechanism termed the Universal Defensive Underpainting Patch (UDUP) that modifies the underpainting of text images instead of the characters.
Optical Character Recognition
Optical Character Recognition (OCR)
SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence
The key of our approach is to exploit an orientation estimation module with a domain adaptive discriminator to align the orientations of point cloud pairs, which significantly alleviates the mispredictions of symmetrical parts.
Ranked #2 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
Query Refinement Transformer for 3D Instance Segmentation
Additionally, we design an affiliated transformer decoder that suppresses the interference of noise background queries and helps the foreground queries focus on instance discriminative parts to predict final segmentation results.
Ranked #4 on
3D Instance Segmentation
on ScanNet(v2)
