Singh, Richa

Audio has become an increasingly crucial biometric modality due to its ability to provide an intuitive way for humans to interact with machines. It is currently being used for a range of applications including person authentication to banking to virtual assistants. Research has shown that these systems are also susceptible to spoofing and attacks. Therefore, protecting audio processing systems against fraudulent activities such as identity theft, financial fraud, and spreading misinformation, is of paramount importance. This paper reviews the current state-of-the-art techniques for detecting audio spoofing and discusses the current challenges along with open research problems. The paper further highlights the importance of considering the ethical and privacy implications of audio spoofing detection systems. Lastly, the work aims to accentuate the need for building more robust and generalizable methods, the integration of automatic speaker verification and countermeasure systems, and better evaluation protocols.

Malnutrition among infants and young children is a pervasive public health concern, particularly in developing countries where resources are limited. Millions of children globally suffer from malnourishment and its complications. Despite the best efforts of governments and organizations, malnourishment persists and remains a leading cause of morbidity and mortality among children under five. Physical measurements, such as weight, height, middle-upper-arm-circumference (muac), and head circumference are commonly used to assess the nutritional status of children. However, this approach can be resource-intensive and challenging to carry out on a large scale. In this research, we are developing NutriAI, a low-cost solution that leverages small sample size classification approach to detect malnutrition by analyzing 2D images of the subjects in multiple poses. The proposed solution will not only reduce the workload of health workers but also provide a more efficient means of monitoring the nutritional status of children. On the dataset prepared as part of this research, the baseline results highlight that the modern deep learning approaches can facilitate malnutrition detection via anthropometric indicators in the presence of diversity with respect to age, gender, physical characteristics, and accessories including clothing.

Advances in deep learning have enabled the creation of photo-realistic DeepFakes by switching the identity or expression of individuals. Such technology in the wrong hands can seed chaos through blackmail, extortion, and forging false statements of influential individuals. This work proposes a novel approach to detect forged videos by magnifying their temporal inconsistencies. A study is also conducted to understand role of ethnicity bias due to skewed datasets on deepfake detection. A new dataset comprising forged videos of Indian ethnicity individuals is presented to facilitate this study.

The spread of fake news poses a critical problem in today's world, where most individuals consume information from online platforms. Fake news detection is an arduous task, marred by the lack of a robust ground truth database for training classification models. Fake news articles manipulate multimedia content (text and images) to disseminate false information. Existing fake news datasets are either small in size or predominantly contain unimodal data. We propose two novel benchmark multimodal datasets, consisting of text and images, to enhance the quality of fake news detection. The first dataset includes manually collected real and fake news data from multiple online sources. In the second dataset, we study the effect of data augmentation by using a Bag of Words approach to increase the quantity of fake news data. These datasets are significantly larger in size in comparison to the existing datasets. We conducted extensive experiments by training state of the art unimodal and multimodal fake news detection algorithms on our dataset and comparing it with the results on existing datasets, showing the effectiveness of our proposed datasets. The experimental results show that data augmentation to increase the quantity of fake news does not hamper the accuracy of fake news detection. The results also conclude that the utilization of multimodal data for fake news detection substantially outperforms the unimodal algorithms.

The solutions proposed in the literature for adversarial robustness are either not effective against the challenging gradient-based attacks or are computationally demanding, such as adversarial training. Adversarial training or network training based data augmentation shows the potential to increase the adversarial robustness. While the training seems compelling, it is not feasible for resource-constrained institutions, especially academia, to train the network from scratch multiple times. The two fold contributions are: (i) providing an effective solution against white-box adversarial attacks via network fine-tuning steps and (ii) observing the role of different optimizers towards robustness. Extensive experiments are performed on a range of databases, including Fashion-MNIST and a subset of ImageNet. It is found that the few steps of network fine-tuning effectively increases the robustness of both shallow and deep architectures. To know other interesting observations, especially regarding the role of the optimizer, refer to the paper.

Latent fingerprint recognition involves acquisition and comparison of latent fingerprints with an exemplar gallery of fingerprints. The diversity in the type of surface leads to different procedures to recover the latent fingerprint. The appearance of latent fingerprints vary significantly due to the development techniques, leading to large intra-class variation. Due to lack of large datasets acquired using multiple mechanisms and surfaces, existing algorithms for latent fingerprint enhancement and comparison may perform poorly. In this study, we propose a Multi-Surface Multi-Technique (MUST) Latent Fingerprint Database. The database consists of more than 16,000 latent fingerprint impressions from 120 unique classes (120 fingers from 12 participants). Including corresponding exemplar fingerprints (livescan and rolled) and extended gallery, the dataset has nearly 21,000 impressions. It has latent fingerprints acquired under 35 different scenarios and additional four subsets of exemplar prints captured using live scan sensor and inked-rolled prints. With 39 different subsets, the database illustrates intra-class variations in latent fingerprints. The database has a potential usage towards building robust algorithms for latent fingerprint enhancement, segmentation, comparison, and multi-task learning. We also provide annotations for manually marked minutiae, acquisition Pixel Per Inch (PPI), and semantic segmentation masks. We also present the experimental protocol and the baseline results for the proposed dataset. The availability of the proposed database can encourage research in handling intra-class variation in latent fingerprint recognition.

Existing facial analysis systems have been shown to yield biased results against certain demographic subgroups. Due to its impact on society, it has become imperative to ensure that these systems do not discriminate based on gender, identity, or skin tone of individuals. This has led to research in the identification and mitigation of bias in AI systems. In this paper, we encapsulate bias detection/estimation and mitigation algorithms for facial analysis. Our main contributions include a systematic review of algorithms proposed for understanding bias, along with a taxonomy and extensive overview of existing bias mitigation algorithms. We also discuss open challenges in the field of biased facial analysis.

Recent advancements in Generative Adversarial Networks (GANs) have made it possible to obtain high-quality face images of synthetic identities. These networks see large amounts of real faces in order to learn to generate realistic looking synthetic images. However, the concept of a synthetic identity for these images is not very well-defined. In this work, we verify identity leakage from the training set containing real images into the latent space and propose a novel method, IdProv, that uses image composition to trace the source of identity signals in the generated image.

Recent work in neural architecture search has spawned interest in algorithms that can predict the performance neural networks using minimum time and computation resources. We propose a new framework, Network Epoch Accuracy Prediction Framework (NEAP-F) which can predict the testing accuracy achieved by a convolutional neural network in one or more epochs. We introduce a novel approach to generate vector representations for networks, and encode “ease” of classifying image datasets into a vector. For vector representations of networks, we focus on the layer parameters and connections between the network layers. A network achieves different accuracy on different image datasets; therefore, we use the image dataset characteristics to create a vector signifying the “ease” of classifying the image dataset. After generating these vectors, the prediction models are trained with architectures having skip connections seen in current state-of-the-art architectures. The framework predicts accuracies in order of milliseconds, demonstrating its computational efficiency. It can be easily applied to neural architecture search methods to predict the performance of candidate networks and can work on unseen datasets as well.