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Found 38 papers, 10 papers with code
TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization
The evaluation datasets and metrics for image manipulation detection and localization (IMDL) research have been standardized.
SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping
However, the training process without utilizing any supervision signal have been proved to be inadequate for disentanglement representation learning.
Weakly Supervised Invariant Representation Learning Via Disentangling Known and Unknown Nuisance Factors
Disentangled and invariant representations are two critical goals of representation learning and many approaches have been proposed to achieve either one of them.
CAT: Controllable Attribute Translation for Fair Facial Attribute Classification
As the social impact of visual recognition has been under scrutiny, several protected-attribute balanced datasets emerged to address dataset bias in imbalanced datasets.
P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking
The designs also reduce the sensor and total energy (obtained from in-house circuit simulations at Globalfoundries 22nm technology node) per frame by 5. 7x and 1. 14x, respectively.
Explaining Face Presentation Attack Detection Using Natural Language
Due to the limited amount of annotated data in our study, we apply a light-weight LSTM network as our natural language generation model.
Information-Theoretic Bias Assessment Of Learned Representations Of Pretrained Face Recognition
As equality issues in the use of face recognition have garnered a lot of attention lately, greater efforts have been made to debiased deep learning models to improve fairness to minorities.
Partner-Assisted Learning for Few-Shot Image Classification
In this paper, we focus on the design of training strategy to obtain an elemental representation such that the prototype of each novel class can be estimated from a few labeled samples.
SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentation
Unlike conventional zero-shot classification, zero-shot semantic segmentation predicts a class label at the pixel level instead of the image level.
Detection and Continual Learning of Novel Face Presentation Attacks
Advances in deep learning, combined with availability of large datasets, have led to impressive improvements in face presentation attack detection research.
MUSCLE: Strengthening Semi-Supervised Learning Via Concurrent Unsupervised Learning Using Mutual Information Maximization
We also show that MUSCLE has the potential to boost the classification performance when used in the fine-tuning phase for a model pre-trained only on unlabeled data.
Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems
Robust speaker recognition, including in the presence of malicious attacks, is becoming increasingly important and essential, especially due to the proliferation of several smart speakers and personal agents that interact with an individual's voice commands to perform diverse, and even sensitive tasks.
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.
CORD19STS: COVID-19 Semantic Textual Similarity Dataset
To overcome this gap, we introduce CORD19STS dataset which includes 13, 710 annotated sentence pairs collected from COVID-19 open research dataset (CORD-19) challenge.
Multispectral Biometrics System Framework: Application to Presentation Attack Detection
In this work, we present a general framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources.
Multi-Modal Fingerprint Presentation Attack Detection: Evaluation On A New Dataset
Our evaluation compares different combination of the new sensing modalities to legacy data from one of our collections as well as the public LivDet2015 dataset, showing the superiority of the new sensing modalities in most cases.
Discovery and Separation of Features for Invariant Representation Learning
Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization.
Invariant Representations through Adversarial Forgetting
We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism.
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.
On the Effectiveness of Laser Speckle Contrast Imaging and Deep Neural Networks for Detecting Known and Unknown Fingerprint Presentation Attacks
To examine the effect of the presence of an unseen attack, we apply a leave-one-attack out strategy.
Unified Adversarial Invariance
We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can additionally use labeled information about biasing factors to force their removal from the latent embedding for making fair predictions.
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.
QATM: Quality-Aware Template Matching For Deep Learning
Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification \etc.
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.
Image-to-GPS Verification Through A Bottom-Up Pattern Matching Network
The image-to-GPS verification problem asks whether a given image is taken at a claimed GPS location.
Unsupervised Adversarial Invariance
Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from learning associations between these factors and the targets, thus reducing overfitting.
BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization
We introduce a novel deep neural architecture for image copy-move forgery detection (CMFD), code-named BusterNet.
Deep Multimodal Image-Repurposing Detection
Nefarious actors on social media and other platforms often spread rumors and falsehoods through images whose metadata (e. g., captions) have been modified to provide visual substantiation of the rumor/falsehood.
Adversarial Auto-encoders for Speech Based Emotion Recognition
Recently, generative adversarial networks and adversarial autoencoders have gained a lot of attention in machine learning community due to their exceptional performance in tasks such as digit classification and face recognition.
CapsuleGAN: Generative Adversarial Capsule Network
We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models.
Bidirectional Conditional Generative Adversarial Networks
Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples ($x$) conditioned on both latent variables ($z$) and known auxiliary information ($c$).
Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text
In this paper, we present a novel deep learning-based approach for assessing the semantic integrity of multimedia packages containing images and captions, using a reference set of multimedia packages.
Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection
Here the task is to estimate the probability that the donor image has been used to splice the query image, and obtain the splicing masks for both the query and donor images.
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
A Graphical Model Approach for Matching Partial Signatures
In this paper, we present a novel partial signature matching method using graphical models.
Learning Document Image Binarization from Data
In this paper we present a fully trainable binarization solution for degraded document images.
