Puspita Majumdar

Bio: Puspita Majumdar is an academic researcher from Indraprastha Institute of Information Technology. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 4, co-authored 19 publications receiving 46 citations. Previous affiliations of Puspita Majumdar include Indian Institute of Technology Delhi.

Evading Face Recognition via Partial Tampering of Faces

Abstract: Advancements in machine learning and deep learning techniques have led to the development of sophisticated and accurate face recognition systems. However, for the past few years, researchers are exploring the vulnerabilities of these systems towards digital attacks. Creation of digitally altered images has become an easy task with the availability of various image editing tools and mobile application such as Snapchat. Morphing based digital attacks are used to elude and gain the identity of legitimate users by fooling the deep networks. In this research, partial face tampering attack is proposed, where facial regions are replaced or morphed to generate tampered samples. Face verification experiments performed using two state-of-the-art face recognition systems, VGG-Face and OpenFace on the CMU-MultiPIE dataset indicates the vulnerability of these systems towards the attack. Further, a Partial Face Tampering Detection (PFTD) network is proposed for the detection of the proposed attack. The network captures the inconsistencies among the original and tampered images by combining the raw and high-frequency information of the input images for the detection of tampered images. The proposed network surpasses the performance of the existing baseline deep neural networks for tampered image detection.

Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images

Abstract: With increasing number of COVID-19 cases globally, all the countries are ramping up the testing numbers. While the RT-PCR kits are available in sufficient quantity in several countries, others are facing challenges with limited availability of testing kits and processing centers in remote areas. This has motivated researchers to find alternate methods of testing which are reliable, easily accessible and faster. Chest X-Ray is one of the modalities that is gaining acceptance as a screening modality. Towards this direction, the paper has two primary contributions. Firstly, we present the COVID-19 Multi-Task Network which is an automated end-to-end network for COVID-19 screening. The proposed network not only predicts whether the CXR has COVID-19 features present or not, it also performs semantic segmentation of the regions of interest to make the model explainable. Secondly, with the help of medical professionals, we manually annotate the lung regions of 9000 frontal chest radiographs taken from ChestXray-14, CheXpert and a consolidated COVID-19 dataset. Further, 200 chest radiographs pertaining to COVID-19 patients are also annotated for semantic segmentation. This database will be released to the research community.

Detecting GANs and Retouching Based Digital Alterations via DAD-HCNN

Abstract: While image generation and editing technologies such as Generative Adversarial Networks and Photoshop are being used for creative and positive applications, the misuse of these technologies to create negative applications including Deep-nude and fake news is also increasing at a rampant pace. Therefore, detecting digitally created and digitally altered images is of paramount importance. This paper proposes a hierarchical approach termed as DAD-HCNN which performs two-fold task: (i) it differentiates between digitally generated images and digitally retouched images from the original unaltered images, and (ii) to increase the explainability of the decision, it also identifies the GAN architecture used to create the image. The effectiveness of the model is demonstrated on a database generated by combining face images generated from four different GAN architectures along with the retouched images and original images from existing benchmark databases.

Data Fine-Tuning

Abstract: In real-world applications, commercial off-the-shelf systems are utilized for performing automated facial analysis including face recognition, emotion recognition, and attribute prediction. However, a majority of these commercial systems act as black boxes due to the inaccessibility of the model parameters which makes it challenging to fine-tune the models for specific applications. Stimulated by the advances in adversarial perturbations, this research proposes the concept of Data Fine-tuning to improve the classification accuracy of a given model without changing the parameters of the model. This is accomplished by modeling it as data (image) perturbation problem. A small amount of “noise” is added to the input with the objective of minimizing the classification loss without affecting the (visual) appearance. Experiments performed on three publicly available datasets LFW, CelebA, and MUCT, demonstrate the effectiveness of the proposed concept.

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Abstract: Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts. Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images and this Analysis identifies over 2,200 relevant published papers and preprints in this area. After initial screening, 62 studies are analysed and the authors find they all have methodological flaws standing in the way of clinical utility. The authors have several recommendations to address these issues.

MIPGAN—Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN

Abstract: 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 be verified against contributing data subjects with a reasonable success rate, given they have a high degree of facial resemblance. The success of morphing attacks is directly dependent on the quality of the generated morph images. We present a new approach for generating strong 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 facial image with minimal artefacts and with high resolution. We demonstrate the proposed approach’s applicability to generate strong morphing attacks by evaluating its vulnerability against both commercial and deep learning based 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 poses a high threat to FRS.

On the Robustness of Face Recognition Algorithms Against Attacks and Bias

Abstract: Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models