VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems
The widespread and rapid adoption of AI-generated content, created by models such as Generative Adversarial Networks (GANs) and Diffusion Models, has revolutionized the digital media landscape by allowing efficient and creative content generation. However, these models also blur the difference between real images and AI-generated synthetic images, raising concerns regarding content authenticity and integrity. While many existing solutions to detect fake images focus solely on classification and higher-resolution images, they often lack transparency in their decision-making, making it difficult for users to understand why an image is classified as fake. In this paper, we present VERITAS, a comprehensive framework that not only accurately detects whether a small (32x32) image is AI-generated but also explains why it was classified that way through artifact localization and semantic reasoning. VERITAS produces human-readable explanations that describe key artifacts in synthetic images. We show that this architecture offers clear explanations of the basis of zero-shot synthetic image detection tasks. Code and relevant prompts can be found at https://github.com/V-i-g-n-e-s-h-N/VERITAS .
Code (1)
Tasks
Decision MakingSynthetic Image DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Versatile Verification of Tree Ensembles
Machine learned models often must abide by certain requirements (e.g., fairness or legal). This has spurred interested in developing approaches that can provably verify whether a model satisfies certain properties. This …
FairnessSVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions
Speaker verification (SV) models are increasingly integrated into security, personalization, and access control systems, yet their robustness to many real-world challenges remains inadequately benchmarked. These include …
Speaker VerificationPyVeritas: On Verifying Python via LLM-Based Transpilation and Bounded Model Checking for C
Python has become the dominant language for general-purpose programming, yet it lacks robust tools for formal verification. In contrast, programmers working in languages such as C benefit from mature model checkers, for …
Fault DiagnosisVERITAS: Towards a General-Purpose Replication Tool for Scientific Research
AI tools are accelerating scientific publication while the systems that review it struggle to keep up, and independent verification of published research has become both harder and more important. As manual replication i…
VERITAS: A Unified Approach to Reliability Evaluation
Large language models (LLMs) often fail to synthesize information from their context to generate an accurate response. This renders them unreliable in knowledge intensive settings where reliability of the output is key. …
Fact CheckingHallucinationQuestion Answering