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Found 45 papers, 16 papers with code
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i. e., inaccessible gradient and parameter information to attackers.
Exploiting the Textual Potential from Vision-Language Pre-training for Text-based Person Search
Text-based Person Search (TPS), is targeted on retrieving pedestrians to match text descriptions instead of query images.
DistilPose: Tokenized Pose Regression with Heatmap Distillation
Specifically, DistilPose maximizes the transfer of knowledge from the teacher model (heatmap-based) to the student model (regression-based) through Token-distilling Encoder (TDE) and Simulated Heatmaps.
Delving into the Adversarial Robustness of Federated Learning
In this work, we propose a novel algorithm called Decision Boundary based Federated Adversarial Training (DBFAT), which consists of two components (local re-weighting and global regularization) to improve both accuracy and robustness of FL systems.
Artificial Intelligence Security Competition (AISC)
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
Shape Matters: Deformable Patch Attack
Though deep neural networks (DNNs) have demonstrated excellent performance in computer vision, they are susceptible and vulnerable to carefully crafted adversarial examples which can mislead DNNs to incorrect outputs.
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition
For face recognition attacks, existing methods typically generate the l_p-norm perturbations on pixels, however, resulting in low attack transferability and high vulnerability to denoising defense models.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation
Existing methods mainly extract the text information from only one sentence to represent an image and the text representation effects the quality of the generated image well.
Towards Stable Co-saliency Detection and Object Co-segmentation
In this paper, we present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG).
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement
In this paper, we propose an efficient structure named Efficient Correspondence Transformer (ECO-TR) by finding correspondences in a coarse-to-fine manner, which significantly improves the efficiency of functional correspondence model.
Federated Learning with Label Distribution Skew via Logits Calibration
Extensive experiments on federated datasets and real-world datasets demonstrate that FedLC leads to a more accurate global model and much improved performance.
Domain Adaptive Person Search
In this paper, we take a further step and present Domain Adaptive Person Search (DAPS), which aims to generalize the model from a labeled source domain to the unlabeled target domain.
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing
Existing DG-based FAS approaches always capture the domain-invariant features for generalizing on the various unseen domains.
Generative Domain Adaptation for Face Anti-Spoofing
Most existing UDA FAS methods typically fit the trained models to the target domain via aligning the distribution of semantic high-level features.
DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain
To compensate, the method introduces a plug-in interactive block to allow attention transfer from the client-side by producing a feature mask.
Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain
This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain.
Evaluation-oriented Knowledge Distillation for Deep Face Recognition
Inspired by the ultimate goal of KD methods, we propose a novel Evaluation oriented KD method (EKD) for deep face recognition to directly reduce the performance gap between the teacher and student models during training.
Exploring Frequency Adversarial Attacks for Face Forgery Detection
In addition, the proposed frequency attack enhances the transferability across face forgery detectors as black-box attacks.
ContrastMask: Contrastive Learning to Segment Every Thing
In this framework, annotated masks of seen categories and pseudo masks of unseen categories serve as a prior for contrastive learning, where features from the mask regions (foreground) are pulled together, and are contrasted against those from the background, and vice versa.
Towards Practical Certifiable Patch Defense with Vision Transformer
To move towards a practical certifiable patch defense, we introduce Vision Transformer (ViT) into the framework of Derandomized Smoothing (DS).
Geometric Synthesis: A Free lunch for Large-scale Palmprint Recognition Model Pretraining
In this paper, by observing that palmar creases are the key information to deep-learning-based palmprint recognition, we propose to synthesize training data by manipulating palmar creases.
Towards Efficient Data Free Black-Box Adversarial Attack
The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate.
End-to-End Reconstruction-Classification Learning for Face Forgery Detection
Reconstruction learning over real images enhances the learned representations to be aware of forgery patterns that are even unknown, while classification learning takes the charge of mining the essential discrepancy between real and fake images, facilitating the understanding of forgeries.
Detecting Camouflaged Object in Frequency Domain
We first design a novel frequency enhancement module (FEM) to dig clues of camouflaged objects in the frequency domain.
Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing
However, the improvement is still limited by two issues: 1) It is difficult to perfectly map all faces to a shared feature space.
Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learning
The progressive enhancement process facilitates the learning of discriminative features with fine-grained face forgery clues.
Dual Contrastive Learning for General Face Forgery Detection
With various facial manipulation techniques arising, face forgery detection has drawn growing attention due to security concerns.
DENSE: Data-Free One-Shot Federated Learning
One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round.
Highly Efficient Natural Image Matting
With a lightweight basic convolution block, we build a two-stages framework: Segmentation Network (SN) is designed to capture sufficient semantics and classify the pixels into unknown, foreground and background regions; Matting Refine Network (MRN) aims at capturing detailed texture information and regressing accurate alpha values.
Spatiotemporal Inconsistency Learning for DeepFake Video Detection
To address this issue, we term this task as a Spatial-Temporal Inconsistency Learning (STIL) process and instantiate it into a novel STIL block, which consists of a Spatial Inconsistency Module (SIM), a Temporal Inconsistency Module (TIM), and an Information Supplement Module (ISM).
Disentangled High Quality Salient Object Detection
As a pixel-wise classification task, LRSCN is designed to capture sufficient semantics at low-resolution to identify the definite salient, background and uncertain image regions.
Ranked #6 on
RGB Salient Object Detection
on DAVIS-S
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing
However, little attention has been paid to the feature extraction process for the FAS task, especially the influence of normalization, which also has a great impact on the generalization of the learned representation.
Structure Destruction and Content Combination for Face Anti-Spoofing
In pursuit of consolidating the face verification systems, prior face anti-spoofing studies excavate the hidden cues in original images to discriminate real persons and diverse attack types with the assistance of auxiliary supervision.
Dual Reweighting Domain Generalization for Face Presentation Attack Detection
Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention due to their robustness for unseen scenarios.
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition
Deep neural networks, particularly face recognition models, have been shown to be vulnerable to both digital and physical adversarial examples.
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing
Face anti-spoofing approach based on domain generalization(DG) has drawn growing attention due to its robustness forunseen scenarios.
Local Relation Learning for Face Forgery Detection
Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions and forms a robust and generalized similarity pattern.
Delving into Data: Effectively Substitute Training for Black-box Attack
Previous substitute training approaches focus on stealing the knowledge of the target model based on real training data or synthetic data, without exploring what kind of data can further improve the transferability between the substitute and target models.
DeeperForensics Challenge 2020 on Real-World Face Forgery Detection: Methods and Results
This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection.
Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System
Face authentication on mobile end has been widely applied in various scenarios.
Face Anti-Spoofing Via Disentangled Representation Learning
Face anti-spoofing is crucial to security of face recognition systems.
Anomaly Detection by One Class Latent Regularized Networks
Anomaly detection is a fundamental problem in computer vision area with many real-world applications.
Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection
In this paper, we propose a light reflection based face anti-spoofing method named Aurora Guard (AG), which is fast, simple yet effective that has already been deployed in real-world systems serving for millions of users.
Learning deep representation from coarse to fine for face alignment
In this paper, we propose a novel face alignment method that trains deep convolutional network from coarse to fine.
