3 citations · 12 across the 14 of their papers we have counts for
19 papers
PREEMPT: Proactive Robustness Evaluation under Editing for Media Protection Techniques
Qiuyu Tang, Aparna Bharati
Proactive image protection methods aim to defend visual content against unauthorized manipulation and generative misuse by embedding protective signals into images before release.…
Right Looks, Wrong Reasons: Compositional Fidelity in Text-to-Image Generation
Mayank Vatsa, Aparna Bharati, Richa Singh
The architectural blueprint of today's leading text-to-image models contains a fundamental flaw: an inability to handle logical composition. This survey investigates this breakdown…
StyleProtect: Safeguarding Artistic Identity in Fine-tuned Diffusion Models
Qiuyu Tang, Joshua Krinsky, Aparna Bharati
The rapid advancement of generative models, particularly diffusion-based approaches, has inadvertently facilitated their potential for misuse. Such models enable malicious exploite…
Is Perturbation-Based Image Protection Disruptive to Image Editing?
Qiuyu Tang, Bonor Ayambem, Mooi Choo Chuah +1
The remarkable image generation capabilities of state-of-the-art diffusion models, such as Stable Diffusion, can also be misused to spread misinformation and plagiarize copyrighted…
From No to Know: Taxonomy, Challenges, and Opportunities for Negation Understanding in Multimodal Foundation Models
Mayank Vatsa, Aparna Bharati, Surbhi Mittal +1
Negation, a linguistic construct conveying absence, denial, or contradiction, poses significant challenges for multilingual multimodal foundation models. These models excel in task…
Learn "No" to Say "Yes" Better: Improving Vision-Language Models via Negations
Jaisidh Singh, Ishaan Shrivastava, Mayank Vatsa +2
Existing vision-language models (VLMs) treat text descriptions as a unit, confusing individual concepts in a prompt and impairing visual semantic matching and reasoning. An importa…
Exploring Saliency Bias in Manipulation Detection
Joshua Krinsky, Alan Bettis, Qiuyu Tang +2
The social media-fuelled explosion of fake news and misinformation supported by tampered images has led to growth in the development of models and datasets for image manipulation d…
Subjective Face Transform using Human First Impressions
Chaitanya Roygaga, Joshua Krinsky, Kai Zhang +2
Humans tend to form quick subjective first impressions of non-physical attributes when seeing someone's face, such as perceived trustworthiness or attractiveness. To understand wha…
A Computer Vision Method for Estimating Velocity from Jumps
Soumyadip Roy, Chaitanya Roygaga, Nathaniel Blanchard +1
Athletes routinely undergo fitness evaluations to evaluate their training progress. Typically, these evaluations require a trained professional who utilizes specialized equipment l…
Learning Transformation-Aware Embeddings for Image Forensics
Aparna Bharati, Daniel Moreira, Patrick Flynn +3
A dramatic rise in the flow of manipulated image content on the Internet has led to an aggressive response from the media forensics research community. New efforts have incorporate…
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval
Joel Brogan, Aparna Bharati, Daniel Moreira +4
Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working onContent-Ba…
Getting the subtext without the text: Scalable multimodal sentiment classification from visual and acoustic modalities
Nathaniel Blanchard, Daniel Moreira, Aparna Bharati +1
In the last decade, video blogs (vlogs) have become an extremely popular method through which people express sentiment. The ubiquitousness of these videos has increased the importa…
Beyond Pixels: Image Provenance Analysis Leveraging Metadata
Aparna Bharati, Daniel Moreira, Joel Brogan +5
Creative works, whether paintings or memes, follow unique journeys that result in their final form. Understanding these journeys, a process known as "provenance analysis", provides…
Image Provenance Analysis at Scale
Daniel Moreira, Aparna Bharati, Joel Brogan +6
Prior art has shown it is possible to estimate, through image processing and computer vision techniques, the types and parameters of transformations that have been applied to the c…
Demography-based Facial Retouching Detection using Subclass Supervised Sparse Autoencoder
Aparna Bharati, Mayank Vatsa, Richa Singh +2
Digital retouching of face images is becoming more widespread due to the introduction of software packages that automate the task. Several researchers have introduced algorithms to…
Provenance Filtering for Multimedia Phylogeny
Allan Pinto, Daniel Moreira, Aparna Bharati +5
Departing from traditional digital forensics modeling, which seeks to analyze single objects in isolation, multimedia phylogeny analyzes the evolutionary processes that influence d…
U-Phylogeny: Undirected Provenance Graph Construction in the Wild
Aparna Bharati, Daniel Moreira, Allan Pinto +5
Deriving relationships between images and tracing back their history of modifications are at the core of Multimedia Phylogeny solutions, which aim to combat misinformation through…
Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization
Joel Brogan, Paolo Bestagini, Aparna Bharati +6
As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, w…
To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?
Sandipan Banerjee, Joel Brogan, Janez Krizaj +5
Face recognition performance has improved remarkably in the last decade. Much of this success can be attributed to the development of deep learning techniques such as convolutional…