arXiv subjects

Jan Zahálka

Publications and source records attributed to Jan Zahálka.

8 recordsLinked to original sources

arXiv2026

Multimedia and Visual Analytics in the Agentic Era

Professional users need tools to help them gain actionable insights from large multimedia collections. Foundation models and AI agents have rapidly changed the playing field, and improving their accuracy, trustworthiness, and reasoning capabilities are active topics in the computer vision, machine learning, and multimedia communities. Most current research focuses on benchmark driven algorithmic improvements. The multimedia community is the place to go beyond algorithms and consider complete multimedia analytics systems that support professional users in their complex tasks and achieve a true teaming of humans and AI. Supporting users with machine learning and visualizations has been studied for decades in the visual analytics field. In this paper, we propose a framework to bring multimedia and visual analytics together and indicate how it could impact current and new multimedia analytics solutions. Additional information can be found at https://staff.fnwi.uva.nl/m.worring/analytics-model.html

cs.MM↗

arXiv2024

Adversarial Attacks on Hyperbolic Networks

As hyperbolic deep learning grows in popularity, so does the need for adversarial robustness in the context of such a non-Euclidean geometry. To this end, this paper proposes hyperbolic alternatives to the commonly used FGM and PGD adversarial attacks. Through interpretable synthetic benchmarks and experiments on existing datasets, we show how the existing and newly proposed attacks differ. Moreover, we investigate the differences in adversarial robustness between Euclidean and fully hyperbolic networks. We find that these networks suffer from different types of vulnerabilities and that the newly proposed hyperbolic attacks cannot address these differences. Therefore, we conclude that the shifts in adversarial robustness are due to the models learning distinct patterns resulting from their different geometries.

cs.LG↗

arXiv2024

A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models

As large language models (LLMs) permeate more and more applications, an assessment of their associated security risks becomes increasingly necessary. The potential for exploitation by malicious actors, ranging from disinformation to data breaches and reputation damage, is substantial. This paper addresses a gap in current research by specifically focusing on security risks posed by LLMs within the prompt-based interaction scheme, which extends beyond the widely covered ethical and societal implications. Our work proposes a taxonomy of security risks along the user-model communication pipeline and categorizes the attacks by target and attack type alongside the commonly used confidentiality, integrity, and availability (CIA) triad. The taxonomy is reinforced with specific attack examples to showcase the real-world impact of these risks. Through this taxonomy, we aim to inform the development of robust and secure LLM applications, enhancing their safety and trustworthiness.

cs.CR↗

arXiv2024

Can ChatGPT Read Who You Are?

The interplay between artificial intelligence (AI) and psychology, particularly in personality assessment, represents an important emerging area of research. Accurate personality trait estimation is crucial not only for enhancing personalization in human-computer interaction but also for a wide variety of applications ranging from mental health to education. This paper analyzes the capability of a generic chatbot, ChatGPT, to effectively infer personality traits from short texts. We report the results of a comprehensive user study featuring texts written in Czech by a representative population sample of 155 participants. Their self-assessments based on the Big Five Inventory (BFI) questionnaire serve as the ground truth. We compare the personality trait estimations made by ChatGPT against those by human raters and report ChatGPT's competitive performance in inferring personality traits from text. We also uncover a 'positivity bias' in ChatGPT's assessments across all personality dimensions and explore the impact of prompt composition on accuracy. This work contributes to the understanding of AI capabilities in psychological assessment, highlighting both the potential and limitations of using large language models for personality inference. Our research underscores the importance of responsible AI development, considering ethical implications such as privacy, consent, autonomy, and bias in AI applications.

cs.CY↗

arXiv2024

Trainwreck: A damaging adversarial attack on image classifiers

Adversarial attacks are an important security concern for computer vision (CV). As CV models are becoming increasingly valuable assets in applied practice, disrupting them is emerging as a form of economic sabotage. This paper opens up the exploration of damaging adversarial attacks (DAAs) that seek to damage target CV models. DAAs are formalized by defining the threat model, the cost function DAAs maximize, and setting three requirements for success: potency, stealth, and customizability. As a pioneer DAA, this paper proposes Trainwreck, a train-time attack that conflates the data of similar classes in the training data using stealthy ($ε\leq 8/255$) class-pair universal perturbations obtained from a surrogate model. Trainwreck is a black-box, transferable attack: it requires no knowledge of the target architecture, and a single poisoned dataset degrades the performance of any model trained on it. The experimental evaluation on CIFAR-10 and CIFAR-100 and various model architectures (EfficientNetV2, ResNeXt-101, and a finetuned ViT-L-16) demonstrates Trainwreck's efficiency. Trainwreck achieves similar or better potency compared to the data poisoning state of the art and is fully customizable by the poison rate parameter. Finally, data redundancy with hashing is identified as a reliable defense against Trainwreck or similar DAAs. The code is available at https://github.com/JanZahalka/trainwreck.

cs.CV↗

arXiv2022

Reproducibility Companion Paper: Describing Subjective Experiment Consistency by $p$-Value P-P Plot

In this paper we reproduce experimental results presented in our earlier work titled "Describing Subjective Experiment Consistency by $p$-Value P-P Plot" that was presented in the course of the 28th ACM International Conference on Multimedia. The paper aims at verifying the soundness of our prior results and helping others understand our software framework. We present artifacts that help reproduce tables, figures and all the data derived from raw subjective responses that were included in our earlier work. Using the artifacts we show that our results are reproducible. We invite everyone to use our software framework for subjective responses analyses going beyond reproducibility efforts.

cs.MM↗

arXiv2020

II-20: Intelligent and pragmatic analytic categorization of image collections

We introduce II-20 (Image Insight 2020), a multimedia analytics approach for analytic categorization of image collections. Advanced visualizations for image collections exist, but they need tight integration with a machine model to support analytic categorization. Directly employing computer vision and interactive learning techniques gravitates towards search. Analytic categorization, however, is not machine classification (the difference between the two is called the pragmatic gap): a human adds/redefines/deletes categories of relevance on the fly to build insight, whereas the machine classifier is rigid and non-adaptive. Analytic categorization that brings the user to insight requires a flexible machine model that allows dynamic sliding on the exploration-search axis, as well as semantic interactions. II-20 brings 3 major contributions to multimedia analytics on image collections and towards closing the pragmatic gap. Firstly, a machine model that closely follows the user's interactions and dynamically models her categories of relevance. II-20's model, in addition to matching and exceeding the state of the art w. r. t. relevance, allows the user to dynamically slide on the exploration-search axis without additional input from her side. Secondly, the dynamic, 1-image-at-a-time Tetris metaphor that synergizes with the model. It allows the model to analyze the collection by itself with minimal interaction from the user and complements the classic grid metaphor. Thirdly, the fast-forward interaction, allowing the user to harness the model to quickly expand ("fast-forward") the categories of relevance, expands the multimedia analytics semantic interaction dictionary. Automated experiments show that II-20's model outperforms the state of the art and also demonstrate Tetris's analytic quality. User studies confirm that II-20 is an intuitive, efficient, and effective multimedia analytics tool.

cs.MM↗

arXiv2019

Exquisitor: Interactive Learning at Large

Increasing scale is a dominant trend in today's multimedia collections, which especially impacts interactive applications. To facilitate interactive exploration of large multimedia collections, new approaches are needed that are capable of learning on the fly new analytic categories based on the visual and textual content. To facilitate general use on standard desktops, laptops, and mobile devices, they must furthermore work with limited computing resources. We present Exquisitor, a highly scalable interactive learning approach, capable of intelligent exploration of the large-scale YFCC100M image collection with extremely efficient responses from the interactive classifier. Based on relevance feedback from the user on previously suggested items, Exquisitor uses semantic features, extracted from both visual and text attributes, to suggest relevant media items to the user. Exquisitor builds upon the state of the art in large-scale data representation, compression and indexing, introducing a cluster-based retrieval mechanism that facilitates the efficient suggestions. With Exquisitor, each interaction round over the full YFCC100M collection is completed in less than 0.3 seconds using a single CPU core. That is 4x less time using 16x smaller computational resources than the most efficient state-of-the-art method, with a positive impact on result quality. These results open up many interesting research avenues, both for exploration of industry-scale media collections and for media exploration on mobile devices.

cs.MM↗