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Hatef Otroshi Shahreza

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Related publications (30)

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On the Information in Deep Biometric Templates: from Vulnerability of Unprotected Templates to Leakage in Protected Templates

Hatef Otroshi Shahreza

Biometric recognition systems tend toward ubiquity and are widely being used in different applications for authentication purposes. Compared to conventional authentication tools, such as PIN or password, which are always in danger of being forgotten or sto ...

EPFL2024

Measuring Linkability of Protected Biometric Templates Using Maximal Leakage

Hatef Otroshi Shahreza, Yanina Yurina Shkel

As the applications of biometric recognition systems are increasing rapidly, there is a growing need to secure the sensitive data used within these systems. Considering privacy challenges in such systems, different biometric template protection (BTP) schem ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Feature-based no-reference video quality assessment using Extra Trees

Arash Amini, Hatef Otroshi Shahreza

With the emergence of social networks and improvements in the internet speed, the video data has become an ever-increasing portion of the global internet traffic. Besides the content, the quality of a video sequence is an important issue at the user end wh ...

WILEY2022

Template Inversion Attack Using Synthetic Face Images Against Real Face Recognition Systems

Hatef Otroshi Shahreza

In this paper, we use synthetic data and propose a new method for template inversion attacks against face recognition systems. We use synthetic data to train a face reconstruction model to generate high-resolution (i.e., 1024 x 1024) face images from facia ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

IRL-Net: Inpainted Region Localization Network via Spatial Attention

Hatef Otroshi Shahreza

Identifying manipulated regions in images is a challenging task due to the existence of very accurate image inpainting techniques leaving almost unnoticeable traces in tampered regions. These image inpainting methods can be used for multiple purposes (e.g. ...

2023

Impulsive noise removal via a blind CNN enhanced by an iterative post-processing

Hatef Otroshi Shahreza, Seyedeh Sahar Sadrizadeh

In digital imaging, especially in the process of data acquisition and transmission, images are often affected by impulsive noise. Therefore, it is essential to remove impulsive noise from images before any further processing. Due to the remarkable performa ...

ELSEVIER2022

On the Recognition Performance of BioHashing on state-of-the-art Face Recognition models

Sébastien Marcel, Hatef Otroshi Shahreza

Face recognition has become a popular authentication tool in recent years. Modern state-of-the-art (SOTA) face recognition methods rely on deep neural networks, which extract discriminative features from face images. Although these methods have high recogn ...

IEEE2021

EdgeFace: Efficient Face Recognition Model for Edge Devices

Hatef Otroshi Shahreza

In this paper, we present EdgeFace - a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt. By effectively combining the strengths of both CNN and Transformer models, and a low rank linear layer, EdgeFace achi ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Inversion of Deep Facial Templates using Synthetic Data

Hatef Otroshi Shahreza

In this paper, we use synthetic data and propose a new method to reconstruct high-resolution face images from facial templates in a template inversion attack against face recognition systems. We use a pre-trained face generator network to generate syntheti ...

IEEE2023

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Sébastien Marcel, Hatef Otroshi Shahreza

State-of-the-art (SOTA) face recognition systems generally use deep convolutional neural networks (CNNs) to extract deep features, called embeddings, from face images. The face embeddings are stored in the system's database and are used for recognition of ...

IEEE2022

Comprehensive Vulnerability Evaluation of Face Recognition Systems to Template Inversion Attacks via 3D Face Reconstruction

Hatef Otroshi Shahreza

In this article, we comprehensively evaluate the vulnerability of state-of-the-art face recognition systems to template inversion attacks using 3D face reconstruction. We propose a new method (called GaFaR) to reconstruct 3D faces from facial templates usi ...

2023

EFaR 2023: Efficient Face Recognition Competition

Hatef Otroshi Shahreza

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of ...

IEEE2023

Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Sébastien Marcel, Hatef Otroshi Shahreza

This study focuses on the protection of soft-biometric attributes related to the demographic information of individuals that can be extracted from compact representations of face images, called embeddings. We consider a state-ofthe-art technology for soft- ...

IEEE COMPUTER SOC2023

Toward High-Resolution Face Image Generation From Coded Aperture Camera

Hatef Otroshi Shahreza

Coded aperture cameras can be manufactured cheaply, have a very thin form-factor, and may be transparent and flexible, thus providing easy-to-integrate and compact image sensors. However, limitations in reconstructed image quality and resolution have imped ...

2023

On Measuring Linkability of Multiple Protected Biometric Templates Using Maximal Leakage

Hatef Otroshi Shahreza, Yanina Yurina Shkel

With the rapid development of biometric recognition systems, users can be simultaneously enrolled in multiple biometric recognition systems, either with a single or multiple biometric characteristics (e.g., face, voice, etc.). With such a growth of biometr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Knowledge Distillation for Face Recognition Using Synthetic Data With Dynamic Latent Sampling

Hatef Otroshi Shahreza

State-of-the-art face recognition models are computationally expensive for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets, raising privacy and ethical concerns. Generating synthetic datasets ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Face Reconstruction from Partially Leaked Facial Embeddings

Hatef Otroshi Shahreza

Face recognition systems are widely used in different applications. In such systems, some features (called templates) are extracted from each face image and stored in the system's database. In this paper, we propose an attack against face recognition syste ...

IEEE2024

Indexing Protected Deep Face Templates by Frequent Binary Patterns

Sébastien Marcel, Hatef Otroshi Shahreza

In this work, we present a simple biometric indexing scheme which is binning and retrieving cancelable deep face templates based on frequent binary patterns. The simplicity of the proposed approach makes it applicable to unprotected as well as protected, i ...

IEEE2022

Vulnerability of State-of-the-Art Face Recognition Models to Template Inversion Attack

Hatef Otroshi Shahreza

Face recognition systems use the templates (extracted from users' face images) stored in the system's database for recognition. In a template inversion attack, the adversary gains access to the stored templates and tries to enter the system using images re ...

Ieee-Inst Electrical Electronics Engineers Inc2024

FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems

Hatef Otroshi Shahreza, Alexander Carl Unnervik, Parsa Rahimi Noshanagh

This article presents FRCSyn-onGoing, an ongoing challenge for face recognition where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases and standard experimental protoc ...

Elsevier2024