Image Hash Minimization for Tamper Detection

Tamper detection using image hash is a very common problem of modern days. Several research and advancements have already been done to address this problem. However, most of the existing methods lack the accuracy of tamper detection when the tampered area is low, as well as requiring long image hashes. In this paper, we propose a novel method objectively to minimize the hash length while enhancing the performance at low tampered area.Comment: Published at the 9th International Conference on Advances in Pattern Recognition, 201

SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation

Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc. However, most of the existing works have ignored the crucial fact regarding the scarcity of labeled data. With growing internet connectivity to personal life, an enormous amount of documents had been available in the public domain and thus making data annotation a tedious task. We address this challenge using self-supervision and unlike, the few existing self-supervised document segmentation approaches which use text mining and textual labels, we use a complete vision-based approach in pre-training without any ground-truth label or its derivative. Instead, we generate pseudo-layouts from the document images to pre-train an image encoder to learn the document object representation and localization in a self-supervised framework before fine-tuning it with an object detection model. We show that our pipeline sets a new benchmark in this context and performs at par with the existing methods and the supervised counterparts, if not outperforms. The code is made publicly available at: https://github.com/MaitySubhajit/SelfDocSegComment: Accepted at The 17th International Conference on Document Analysis and Recognition (ICDAR 2023

DistilDoc: Knowledge Distillation for Visually-Rich Document Applications

This work explores knowledge distillation (KD) for visually-rich document (VRD) applications such as document layout analysis (DLA) and document image classification (DIC). While VRD research is dependent on increasingly sophisticated and cumbersome models, the field has neglected to study efficiency via model compression. Here, we design a KD experimentation methodology for more lean, performant models on document understanding (DU) tasks that are integral within larger task pipelines. We carefully selected KD strategies (response-based, feature-based) for distilling knowledge to and from backbones with different architectures (ResNet, ViT, DiT) and capacities (base, small, tiny). We study what affects the teacher-student knowledge gap and find that some methods (tuned vanilla KD, MSE, SimKD with an apt projector) can consistently outperform supervised student training. Furthermore, we design downstream task setups to evaluate covariate shift and the robustness of distilled DLA models on zero-shot layout-aware document visual question answering (DocVQA). DLA-KD experiments result in a large mAP knowledge gap, which unpredictably translates to downstream robustness, accentuating the need to further explore how to efficiently obtain more semantic document layout awareness.Comment: Accepted to ICDAR 2024 (Athens, Greece

Chitosan nanoparticles for insulin delivery in type 1 diabetes: Overcoming challenges in bioavailability and long-term control

Type 1 diabetes (T1D) cohort requires a lifelong insulin supplement but the traditional insulin delivery systems have severe drawbacks such as unacceptable bioavailability of the drug, frequent regimens, lack of patient adherence. This paper will discuss the use of chitosan nanoparticles (CNPs) as a new form of insulin delivery vehicle, and the potential that this possesses to improve its effectiveness as a form of therapy. CNPs enhance the stability of insulin stabilizing it against enzymatic degradation; they also allow controlled release. On the other hand, their mucoadhesive properties prolong intestinal retention that can improve absorption and reduce dosing frequency, which can improve patient compliance. Again, the CNPs encapsulate insulin through electrostatic interactions that can prevent degradation in the gastrointestinal tract while endorsing sustained glucose regulation. Furthermore, this study specify that CNP-based insulin delivery significantly expands glycemic control and reduces hypoglycemia risks. Despite their advantages, challenges include variability in insulin release, scalability in production, and regulatory hurdles. Future advancements, such as hybrid systems and stimuli-responsive nanoparticles, aim to optimize stability and targeted insulin delivery. The integration of nanomedicine into diabetes management may revolutionize treatment, offering a more effective and patient-friendly approach

Therapeutic Potential of Exploiting Autophagy Cascade Against Coronavirus Infection

Since its emergence in December 2019 in Wuhan, China, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) created a worldwide pandemic of coronavirus disease (COVID-19) with nearly 136 million cases and approximately 3 million deaths. Recent studies indicate that like other coronaviruses, SARS-CoV-2 also hijacks or usurps various host cell machineries including autophagy for its replication and disease pathogenesis. Double membrane vesicles generated during initiation of autophagy cascade act as a scaffold for the assembly of viral replication complexes and facilitate RNA synthesis. The use of autophagy inhibitors - chloroquine and hydroxychloroquine initially appeared to be as a potential treatment strategy of COVID-19 patients but later remained at the center of debate due to high cytotoxic effects. In the absence of a specific drug or vaccine, there is an urgent need for a safe, potent as well as affordable drug to control the disease spread. Given the intricate connection between autophagy machinery and viral pathogenesis, the question arises whether targeting autophagy pathway might show a path to fight against SARS-CoV-2 infection. In this review we will discuss about our current knowledge linking autophagy to coronaviruses and how that is being utilized to repurpose autophagy modulators as potential COVID-19 treatment.</jats:p