Research about the Ability of LLM in the Tamper-Detection Area

• URL: http://arxiv.org/abs/2401.13504v1
• Date: Wed, 24 Jan 2024 14:53:06 GMT
• Title: Research about the Ability of LLM in the Tamper-Detection Area
• Authors: Xinyu Yang and Jizhe Zhou
• Abstract summary: Large Language Models (LLMs) have emerged as the most powerful AI tools in addressing a diverse range of challenges. We have collected five different LLMs developed by various companies: GPT-4, LLaMA, Bard, ERNIE Bot 4.0, and Tongyi Qianwen. Most LLMs can identify composite pictures that are inconsistent with logic, and only more powerful LLMs can distinguish logical, but visible signs of tampering to the human eye.
• Score: 20.620232937684133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, particularly since the early 2020s, Large Language Models (LLMs) have emerged as the most powerful AI tools in addressing a diverse range of challenges, from natural language processing to complex problem-solving in various domains. In the field of tamper detection, LLMs are capable of identifying basic tampering activities.To assess the capabilities of LLMs in more specialized domains, we have collected five different LLMs developed by various companies: GPT-4, LLaMA, Bard, ERNIE Bot 4.0, and Tongyi Qianwen. This diverse range of models allows for a comprehensive evaluation of their performance in detecting sophisticated tampering instances.We devised two domains of detection: AI-Generated Content (AIGC) detection and manipulation detection. AIGC detection aims to test the ability to distinguish whether an image is real or AI-generated. Manipulation detection, on the other hand, focuses on identifying tampered images. According to our experiments, most LLMs can identify composite pictures that are inconsistent with logic, and only more powerful LLMs can distinguish logical, but visible signs of tampering to the human eye. All of the LLMs can't identify carefully forged images and very realistic images generated by AI. In the area of tamper detection, LLMs still have a long way to go, particularly in reliably identifying highly sophisticated forgeries and AI-generated images that closely mimic reality.

Related papers

• Can GPT tell us why these images are synthesized? Empowering Multimodal Large Language Models for Forensics [18.989883830031093]
  multimodal Large Language Models (LLMs) have encoded rich world knowledge but struggle to comprehend local forgery details. We propose a framework capable of evaluating image authenticity, localizing tampered regions, providing evidence, and tracing generation methods based on semantic tampering clues. We conduct qualitative and quantitative experiments and show that GPT4V can achieve an accuracy of 92.1% in Autosplice and 86.3% in LaMa, which is competitive with state-of-the-art AIGC detection methods.
  arXiv  Detail & Related papers  (2025-04-16T01:02:46Z)
• FakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensics [66.14786900470158]
  We propose FakeScope, an expert multimodal model (LMM) tailored for AI-generated image forensics. FakeScope identifies AI-synthetic images with high accuracy and provides rich, interpretable, and query-driven forensic insights. FakeScope achieves state-of-the-art performance in both closed-ended and open-ended forensic scenarios.
  arXiv  Detail & Related papers  (2025-03-31T16:12:48Z)
• VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models [14.053424085561296]
  Face models with high-quality and controllable attributes pose a significant challenge for Deepfake detection. In this work, we integrate Multimodal Large Language Models (MLLMs) within DM-based face forensics. We propose a fine-grained analysis triad framework called VLForgery, that can 1) predict falsified facial images; 2) locate the falsified face regions subjected to partial synthesis; and 3) attribute the synthesis with specific generators.
  arXiv  Detail & Related papers  (2025-03-08T09:55:19Z)
• Survey on AI-Generated Media Detection: From Non-MLLM to MLLM [51.91311158085973]
  Methods for detecting AI-generated media have evolved rapidly. General-purpose detectors based on MLLMs integrate authenticity verification, explainability, and localization capabilities. Ethical and security considerations have emerged as critical global concerns.
  arXiv  Detail & Related papers  (2025-02-07T12:18:20Z)
• Large Language Models Think Too Fast To Explore Effectively [0.0]
  The extent to which Large Language Models can effectively explore, particularly in open-ended tasks, remains unclear. This study investigates whether LLMs can surpass humans in exploration during an open-ended task, using Little Alchemy 2 as a paradigm.
  arXiv  Detail & Related papers  (2025-01-29T21:51:17Z)
• LLMScan: Causal Scan for LLM Misbehavior Detection [6.001414661477911]
  Large Language Models (LLMs) generate untruthful, biased and harmful responses. This work introduces LLMScan, an innovative monitoring technique based on causality analysis.
  arXiv  Detail & Related papers  (2024-10-22T02:27:57Z)
• Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
  Large language models (LLMs) generate content that can undermine trust in online discourse. Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-AI collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
  arXiv  Detail & Related papers  (2024-10-18T08:14:10Z)
• MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs [61.56904387052982]
  This paper proposes a new visual grounding task called multi-context visual grounding. It aims to localize instances of interest across multiple images based on open-ended text prompts. We benchmark over 20 state-of-the-art MLLMs and foundation models with potential multi-context visual grounding capabilities.
  arXiv  Detail & Related papers  (2024-10-16T07:52:57Z)
• ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization [49.992614129625274]
  ForgeryGPT is a novel framework that advances the Image Forgery Detection and localization task. It captures high-order correlations of forged images from diverse linguistic feature spaces. It enables explainable generation and interactive dialogue through a newly customized Large Language Model (LLM) architecture.
  arXiv  Detail & Related papers  (2024-10-14T07:56:51Z)
• VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection [19.79027968793026]
  Zero-shot anomaly detection (ZSAD) recognizes and localizes anomalies in previously unseen objects. Existing ZSAD methods are limited by closed-world settings, struggling to unseen defects with predefined prompts. We propose a novel framework VMAD (Visual-enhanced MLLM Anomaly Detection) that enhances MLLM with visual-based IAD knowledge and fine-grained perception.
  arXiv  Detail & Related papers  (2024-09-30T09:51:29Z)
• DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection [15.933013428603152]
  Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks. We present a benchmark suite designed to assess the performance of LLMs in identifying and mitigating fraudulent and abusive language.
  arXiv  Detail & Related papers  (2024-09-09T21:12:03Z)
• How do LLMs Support Deep Learning Testing? A Comprehensive Study Through the Lens of Image Mutation [23.18635769949329]
  Visual deep learning (VDL) systems have shown significant success in real-world applications like image recognition, object detection, and autonomous driving. To evaluate the reliability of VDL, software testing requires diverse and controllable mutations over image semantics. The rapid development of multi-modal large language models (MLLMs) has introduced revolutionary image mutation potentials through instruction-driven methods.
  arXiv  Detail & Related papers  (2024-04-22T07:41:41Z)
• FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models [62.66610648697744]
  We introduce a taxonomy of generative visual forgery concerning human perception, based on which we collect forgery descriptions in human natural language. FakeBench examines LMMs with four evaluation criteria: detection, reasoning, interpretation and fine-grained forgery analysis. This research presents a paradigm shift towards transparency for the fake image detection area.
  arXiv  Detail & Related papers  (2024-04-20T07:28:55Z)
• Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection [34.135738700682055]
  This paper conducts a comprehensive evaluation of large language models (LLMs) represented by ChatGPT. We find that LLMs exhibit strong zero-shot and few-shot capabilities, but is still at a disadvantage compared to models fine-tuned with full resource.
  arXiv  Detail & Related papers  (2024-02-27T07:02:10Z)
• A Survey on Detection of LLMs-Generated Content [97.87912800179531]
  The ability to detect LLMs-generated content has become of paramount importance. We aim to provide a detailed overview of existing detection strategies and benchmarks. We also posit the necessity for a multi-faceted approach to defend against various attacks.
  arXiv  Detail & Related papers  (2023-10-24T09:10:26Z)
• Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems [61.11799513362704]
  We propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective.
  arXiv  Detail & Related papers  (2023-03-03T02:07:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.