Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks

State-of-the-art generative model-based attacks against image classifiers overwhelmingly focus on single-object (i.e., single dominant object) images. Different from such settings, we tackle a more practical problem of generating adversarial perturbations using multi-object (i.e., multiple dominant objects) images as they are representative of most real-world scenes. Our goal is to design an attack strategy that can learn from such natural scenes by leveraging the local patch differences that occur inherently in such images (e.g. difference between the local patch on the object `person' and the object `bike' in a traffic scene). Our key idea is to misclassify an adversarial multi-object image by confusing the victim classifier for each local patch in the image. Based on this, we propose a novel generative attack (called Local Patch Difference or LPD-Attack) where a novel contrastive loss function uses the aforesaid local differences in feature space of multi-object scenes to optimize the perturbation generator. Through various experiments across diverse victim convolutional neural networks, we show that our approach outperforms baseline generative attacks with highly transferable perturbations when evaluated under different white-box and black-box settings.Comment: Accepted at WACV 2023 (Round 1), camera-ready versio

Non-Adversarial Video Synthesis with Learned Priors

Most of the existing works in video synthesis focus on generating videos using adversarial learning. Despite their success, these methods often require input reference frame or fail to generate diverse videos from the given data distribution, with little to no uniformity in the quality of videos that can be generated. Different from these methods, we focus on the problem of generating videos from latent noise vectors, without any reference input frames. To this end, we develop a novel approach that jointly optimizes the input latent space, the weights of a recurrent neural network and a generator through non-adversarial learning. Optimizing for the input latent space along with the network weights allows us to generate videos in a controlled environment, i.e., we can faithfully generate all videos the model has seen during the learning process as well as new unseen videos. Extensive experiments on three challenging and diverse datasets well demonstrate that our approach generates superior quality videos compared to the existing state-of-the-art methods.Comment: Accepted to CVPR 202

Towards Reliable Learning Systems: Efficient, Secure, and Generalizable Generative Models

Human beings inherently tend to learn skills that generalize well to different environments. The ability to infer or interpret the surroundings efficiently with proper reasoning differentiates humans from other living beings. Moreover, humans possess the ability of tackling scenarios when presented with incorrect information. Therefore, real-world machine learning models should be able to learn from data distribution, possess the ability to utilize information under changing conditions, and be robust against adversaries trying to manipulate their decisions while being compute efficient. This thesis principally focuses towards understanding how robust features can be extracted from underlying data distributions to train models better, explore the extent of brittleness of model decision-making, and reduce the designing cost of multi-task models such that they can be used in diverse scenarios with optimum performance. The first work focuses on the problem of generating videos from latent noise vectors, without any reference input frames. We developed a method that jointly optimizes the input latent space, the weights of a recurrent neural network and a generator through non-adversarial learning. In the second work, we looked into the problem of cross-domain unsupervised video anomaly detection tasks where no target domain training data are available. The goal is to allow end-users in accessing a system that works ``out-of-the-box" to avoid laborious model-tuning. In the third work, we leveraged upon the open-sourced pre-trained vision-language model CLIP (Contrastive Language-Image Pre-training) to create adversarial attack with multi-object scenes. The motivation is to exploit the encoded semantics in the language space along with the visual space. In order to represent the relationships between different objects in natural scenes, we designed an attack approach that demonstrates the utility of the CLIP model as an attacker's tool to train formidable perturbation generators for multi-object scenes. In the fourth work, we proposed a method to deploy multi-task machine learning models on diverse hardware platforms that satisfy multiple hardware efficiency constraints (e.g., storage, latency), while keeping training cost to a minimum. In particular, we present a methodology to learn slimmable multi-task models that can allow switchable filters based on user constraints, without much performance degradation

Adrenal neuroblastoma in an adult

Adrenal neuroblastomas, although quite common in children, are extremely rare in adulthood. Here, we are reporting the case of a 47-year-old male who presented with right flank pain and had a palpable mass in the same region. Contrast-enhanced computed tomography showed an irregular, poorly marginated heterogeneous mass lesion arising from the right suprarenal position. Urinary catecholamines were within normal limits. There was no evidence of metastatic disease. The mass was resected en-block along with the right kidney. Histopathologic examination revealed the diagnosis of a neuroblastoma. Adjuvant chemotherapy was instituted, but the pain recurred after 9months. Despite subsequent chemotherapy, the mass continued to grow over the next 6months with further infiltration into the liver and surrounding muscles. The patient has currently been started on external palliative radiation. We have also reviewed the literature to present a discussion on presentation, diagnosis, and management of this rare tumor