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Found 70 papers, 26 papers with code
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.
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.
Anomaly Detection by One Class Latent Regularized Networks
Anomaly detection is a fundamental problem in computer vision area with many real-world applications.
Face Anti-Spoofing Via Disentangled Representation Learning
Face anti-spoofing is crucial to security of face recognition systems.
Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System
Face authentication on mobile end has been widely applied in various scenarios.
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.
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.
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.
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.
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.
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.
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.
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.
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 #10 on
RGB Salient Object Detection
on DAVIS-S
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).
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.
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.
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.
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.
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.
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.
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.
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.
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 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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
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.
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.
Artificial Intelligence Security Competition (AISC)
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
Probabilistic Knowledge Distillation of Face Ensembles
Mean ensemble (i. e. averaging predictions from multiple models) is a commonly-used technique in machine learning that improves the performance of each individual model.
Rethinking the Learning Paradigm for Dynamic Facial Expression Recognition
Dynamic Facial Expression Recognition (DFER) is a rapidly developing field that focuses on recognizing facial expressions in video format.
Ranked #7 on
Dynamic Facial Expression Recognition
on FERV39k
Dynamic Facial Expression Recognition
Facial Expression Recognition
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.
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.
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.
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.
Instance-Aware Domain Generalization for Face Anti-Spoofing
To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels.
Towards Generalizable Data Protection With Transferable Unlearnable Examples
One of the crucial factors contributing to this success has been the access to an abundance of high-quality data for constructing machine learning models.
Re-thinking Data Availablity Attacks Against Deep Neural Networks
The unauthorized use of personal data for commercial purposes and the clandestine acquisition of private data for training machine learning models continue to raise concerns.
Query-Efficient Decision-based Black-Box Patch Attack
In this work, we first explore the decision-based patch attack.
RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition
Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric.
Towards General Visual-Linguistic Face Forgery Detection
To address this issues, in this paper, we propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
Seeing in Flowing: Adapting CLIP for Action Recognition with Motion Prompts Learning
We propose that the key lies in explicitly modeling the motion cues flowing in video frames.
Continual Face Forgery Detection via Historical Distribution Preserving
In this paper, we focus on a novel and challenging problem: Continual Face Forgery Detection (CFFD), which aims to efficiently learn from new forgery attacks without forgetting previous ones.
Privacy-Preserving Face Recognition Using Random Frequency Components
The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
Contrastive Pseudo Learning for Open-World DeepFake Attribution
The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques.
Generalizable Person Search on Open-world User-Generated Video Content
Person search is a challenging task that involves detecting and retrieving individuals from a large set of un-cropped scene images.
Exploring Decision-based Black-box Attacks on Face Forgery Detection
Further, the fake faces by our method can pass face forgery detection and face recognition, which exposes the security problems of face forgery detectors.
Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning
In detail, CPPF consists of a prototype clustering module (PC), an embedding space reserving module (ESR) and a multi-teacher distillation module (MTD).
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning
On the one hand, to maximize the complementarity of tasks with high similarity, we utilize a gradient-driven task grouping method that partitions tasks into several disjoint groups and assign a group-shared MmAP to each group.
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a significant performance drop on the original tasks.
Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation.
LaRE^2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection
In response to this, we propose a novel Latent REconstruction error guided feature REfinement method (LaRE^2) for detecting the diffusion-generated images.
Test-Time Domain Generalization for Face Anti-Spoofing
Our method, consisting of Test-Time Style Projection (TTSP) and Diverse Style Shifts Simulation (DSSS), effectively projects the unseen data to the seen domain space.
SDPose: Tokenized Pose Estimation via Circulation-Guide Self-Distillation
To mitigate the problem of under-fitting, we design a transformer module named Multi-Cycled Transformer(MCT) based on multiple-cycled forwards to more fully exploit the potential of small model parameters.
Anchor-based Robust Finetuning of Vision-Language Models
Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pretraining data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics.
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
Rethinking Clothes Changing Person ReID: Conflicts, Synthesis, and Optimization
However, such improvement sacrifices the performance under the standard protocol, caused by the inner conflict between standard and CC.
