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Found 21 papers, 13 papers with code
Exploring the Connection between Robust and Generative Models
Though a common assumption is that adversarial points leave the manifold of the input data, our study finds out that, surprisingly, untargeted adversarial points in the input space are very likely under the generative model hidden inside the discriminative classifier -- have low energy in the EBM.
Hierarchical Fine-Grained Image Forgery Detection and Localization
As a result, the algorithm is encouraged to learn both comprehensive features and inherent hierarchical nature of different forgery attributes, thereby improving the IFDL representation.
MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust Classifier
We offer a method for one-shot image synthesis that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers.
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training
The original model shows an accuracy of 59% under AutoAttack - when trained with additional data with pseudo-labels.
Two-branch Recurrent Network for Isolating Deepfakes in Videos
The current spike of hyper-realistic faces artificially generated using deepfakes calls for media forensics solutions that are tailored to video streams and work reliably with a low false alarm rate at the video level.
Towards Learning Structure via Consensus for Face Segmentation and Parsing
Face segmentation is the task of densely labeling pixels on the face according to their semantics.
Does Generative Face Completion Help Face Recognition?
Face occlusions, covering either the majority or discriminative parts of the face, can break facial perception and produce a drastic loss of information.
Recurrent Convolutional Strategies for Face Manipulation Detection in Videos
The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods.
RoPAD: Robust Presentation Attack Detection through Unsupervised Adversarial Invariance
For enterprise, personal and societal applications, there is now an increasing demand for automated authentication of identity from images using computer vision.
AIRD: Adversarial Learning Framework for Image Repurposing Detection
Image repurposing is a commonly used method for spreading misinformation on social media and online forums, which involves publishing untampered images with modified metadata to create rumors and further propaganda.
ExpNet: Landmark-Free, Deep, 3D Facial Expressions
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.
Ranked #1 on
3D Facial Expression Recognition
on 2017_test set
(using extra training data)
Extreme 3D Face Reconstruction: Seeing Through Occlusions
Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e. g., wrinkles).
FacePoseNet: Making a Case for Landmark-Free Face Alignment
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
Ranked #1 on
Facial Landmark Detection
on 300W
(Mean Error Rate metric)
On Face Segmentation, Face Swapping, and Face Perception
To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure the effect of intra- and inter-subject face swapping on recognition.
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
The 3D shapes of faces are well known to be discriminative.
Ranked #4 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)
Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification
In this paper we introduce a method to overcome one of the main challenges of person re-identification in multi-camera networks, namely cross-view appearance changes.
Pooling Faces: Template based Face Recognition with Pooled Face Images
We propose a novel approach to template based face recognition.
Pose-Aware Face Recognition in the Wild
We propose a method to push the frontiers of unconstrained face recognition in the wild, focusing on the problem of extreme pose variations.
Do We Really Need to Collect Millions of Faces for Effective Face Recognition?
Face recognition capabilities have recently made extraordinary leaps.
Ranked #12 on
Face Verification
on IJB-A
Face Recognition Using Deep Multi-Pose Representations
In our representation, a face image is processed by several pose-specific deep convolutional neural network (CNN) models to generate multiple pose-specific features.
Ranked #14 on
Face Verification
on IJB-A
Continuous Localization and Mapping of a Pan Tilt Zoom Camera for Wide Area Tracking
Pan-tilt-zoom (PTZ) cameras are powerful to support object identification and recognition in far-field scenes.
