arXiv subjects

Ritwik Murali

Publications and source records attributed to Ritwik Murali.

4 recordsLinked to original sources

arXiv2026

StructuredEdit: Constraint-Aware Graphic Design Editing via Differentiable Parameter Propagation

Graphic design editing requires precise manipulation of typography, layout, and visual hierarchy under strict design constraints. Following the introduction of large language models, organizations have increasingly promoted vision-language models to enhance productivity. However, current models operate on pixels and achieve only 52% constraint satisfaction on structured design edits, thereby limiting their reliability for professional workflows. We present StructuredEdit, a pipeline that reframes design editing as parameter manipulation rather than pixel generation. Our core technical contribution is Differentiable Parameter Propagation (DPP), a training method that embeds hard design constraints into vision-language model fine-tuning by backpropagating pixel-level constraint violations through a lightweight differentiable rasterizer. A hybrid candidate-and-filter pipeline produces 125k validated edit triplets. The resulting system reaches 89% constraint satisfaction versus 52% for GPT-4V, 0.82 matched-element Intersection over Union, and 76% top-1 font accuracy over the 100 most-frequent design typefaces. In a user study (N=35), editing time drops 33% and correction iterations drop 44% relative to a GPT-4V baseline.

cs.GR↗

arXiv2025

ACM COMPUTE 2025 Best Practices Track Proceedings

COMPUTE is an annual Indian conference supported by ACM India and iSIGCSE. The focus of COMPUTE is to improve the quality of computing education in the country by providing a platform for academicians and researchers to interact and share best practices in teaching, learning, and education in general. The Best Practices Track of COMPUTE 2025 invited Computer Science Educators across the country to submit an experience report for the best practices under multiple categories: 1) Novel classroom activities, 2) Imaginative assignments that promote creativity and problem-solving, 3) Diverse pedagogical approaches (e.g., flipped classrooms, peer teaching, project-based learning), 4) Designing AI-resistant or AI-integrated assessment questions, and 5) Teaching CS to students from other disciplines (e.g., business, humanities, engineering). These proceedings contain papers selected from these submissions for presentation at the conference, as well as a report (written by the editors) from the two best practices sessions where these were presented.

cs.CY↗

arXiv2025

Securing the Software Package Supply Chain for Critical Systems

Software systems have grown as an indispensable commodity used across various industries, and almost all essential services depend on them for effective operation. The software is no longer an independent or stand-alone piece of code written by a developer but rather a collection of packages designed by multiple developers across the globe. Ensuring the reliability and resilience of these systems is crucial since emerging threats target software supply chains, as demonstrated by the widespread SolarWinds hack in late 2020. These supply chains extend beyond patches and updates, involving distribution networks throughout the software lifecycle. Industries like smart grids, manufacturing, healthcare, and finance rely on interconnected software systems and their dependencies for effective functioning. To secure software modules and add-ons, robust distribution architectures are essential. The proposed chapter enhances the existing delivery frameworks by including a permissioned ledger with Proof of Authority consensus and multi-party signatures. The proposed system aims to prevent attacks while permitting every stakeholder to verify the same. Critical systems can interface with the secure pipeline without disrupting existing functionalities, thus preventing the cascading effect of an attack at any point in the supply chain.

cs.SE↗

arXiv2025

Adapting Novelty towards Generating Antigens for Antivirus systems

It is well known that anti-malware scanners depend on malware signatures to identify malware. However, even minor modifications to malware code structure results in a change in the malware signature thus enabling the variant to evade detection by scanners. Therefore, there exists the need for a proactively generated malware variant dataset to aid detection of such diverse variants by automated antivirus scanners. This paper proposes and demonstrates a generic assembly source code based framework that facilitates any evolutionary algorithm to generate diverse and potential variants of an input malware, while retaining its maliciousness, yet capable of evading antivirus scanners. Generic code transformation functions and a novelty search supported quality metric have been proposed as components of the framework to be used respectively as variation operators and fitness function, for evolutionary algorithms. The results demonstrate the effectiveness of the framework in generating diverse variants and the generated variants have been shown to evade over 98% of popular antivirus scanners. The malware variants evolved by the framework can serve as antigens to assist malware analysis engines to improve their malware detection algorithms.

cs.CR↗