-
Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? – Vulnerability and Detection
The primary objective of face morphing is to combine face images of diff...
read it
-
Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks
Morphing attacks is a threat to biometric systems where the biometric re...
read it
-
Generating Photo-Realistic Training Data to Improve Face Recognition Accuracy
In this paper we investigate the feasibility of using synthetic data to ...
read it
-
On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection
Face morphing attacks have raised critical concerns as they demonstrate ...
read it
-
Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
Speech is a rich biometric signal that contains information about the id...
read it
-
Robustness of Facial Recognition to GAN-based Face-morphing Attacks
Face-morphing attacks have been a cause for concern for a number of year...
read it
-
On measuring the iconicity of a face
For a given identity in a face dataset, there are certain iconic images ...
read it
MIPGAN – Generating Robust and High Quality Morph Attacks Using Identity Prior Driven GAN
Face morphing attacks target to circumvent Face Recognition Systems (FRS) by employing face images derived from multiple data subjects (e.g., accomplices and malicious actors). Morphed images can verify against contributing data subjects with a reasonable success rate, given they have a high degree of identity resemblance. The success of the morphing attacks is directly dependent on the quality of the generated morph images. We present a new approach for generating robust attacks extending our earlier framework for generating face morphs. We present a new approach using an Identity Prior Driven Generative Adversarial Network, which we refer to as MIPGAN (Morphing through Identity Prior driven GAN). The proposed MIPGAN is derived from the StyleGAN with a newly formulated loss function exploiting perceptual quality and identity factor to generate a high quality morphed face image with minimal artifacts and with higher resolution. We demonstrate the proposed approach's applicability to generate robust morph attacks by evaluating it against a commercial Face Recognition System (FRS) and demonstrate the success rate of attacks. Extensive experiments are carried out to assess the FRS's vulnerability against the proposed morphed face generation technique on three types of data such as digital images, re-digitized (printed and scanned) images, and compressed images after re-digitization from newly generated MIPGAN Face Morph Dataset. The obtained results demonstrate that the proposed approach of morph generation profoundly threatens the FRS.
READ FULL TEXT
Comments
There are no comments yet.