Fakeshield : Interpretable Image Detection and Localization based on Multimodal Large Language Models

Fakeshield

Image Editing Security #Image Detection #Multimodal Learning #Large Language Models #Interpretability #Cross-Domain Generalization Standard Picks Open Source

Overview :

FakeShield is a multimodal framework designed to address two primary challenges in the field of Image Forensics Detection and Localization (IFDL): the black-box nature of detection mechanisms and the limited generalization across different tampering methods. By leveraging GPT-4o to enhance existing IFDL datasets, FakeShield has created a Multimodal Tampering Description Dataset (MMTD-Set) to train its tampering analysis capabilities. The framework includes domain label-guided interpretable detection modules (DTE-FDM) and localization modules (MFLM) that can interpret various types of tampering detection and guide localization through detailed textual descriptions. FakeShield outperforms other methods in detection accuracy and F1 scores, providing a superior and interpretable solution.

Target Users :

FakeShield is designed for image forensics experts, cybersecurity analysts, and any individuals or organizations that need to detect and locate image tampering. This product enhances user understanding of how and why tampering occurs by providing interpretable detection results and precise localization of tampered areas, thereby improving the credibility and security of image content.

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Use Cases

Cybersecurity firms use FakeShield to detect and localize deepfake screenshots circulating online to identify and halt the spread of misinformation.

News agencies leverage FakeShield to verify the authenticity of news images, ensuring accuracy and fairness in reporting.

Individual users apply FakeShield to analyze images on social media to identify potential image tampering and protect themselves from misinformation.

Features

Domain label guided interpretable detection: Bridges data domain conflicts among different types of data using field-specific labels, guiding the multimodal large language model to generate detection results and reasoning.

Localization module: Uses the description of tampered areas outputted by DTE-FDM as prompts for a visual segmentation model, precisely guiding the localization of tampered areas.

Multimodal Tampering Description Dataset (MMTD-Set): Generates analysis and descriptions of tampered images using GPT-4o, constructing ‘image-mask-description’ triples to support multimodal training of the model.

Cross-domain generalization capability: Effectively manages data conflicts among different tampering types using domain label strategies, enhancing cross-domain generalization.

High-precision detection performance: Demonstrates superior detection accuracy and F1 scores on datasets such as Photoshop and AIGC-Editing.

Detailed interpretability: Assesses FakeShield's interpretability through Cosine Semantic Similarity (CSS), generating tampered area descriptions that closely align with real conditions.

Accurate localization performance: Achieves the highest IoU and F1 scores across multiple test sets, providing clearer and more precise tampered area segmentation.

How to Use

1. Visit the FakeShield website to learn about the product's overview and main features.

2. Read the documentation and tutorials to understand how to use FakeShield for image detection and localization.

3. Download and install any necessary software or plugins to run FakeShield in local or cloud environments.

4. Upload the image files that need to be analyzed to the FakeShield platform.

5. Utilize the DTE-FDM module within FakeShield to perform image detection and obtain results.

6. Use the MFLM module to locate tampered areas in the image based on the outputs from the DTE-FDM.

7. Analyze the descriptions of tampered areas and the image masks provided by FakeShield to gain deeper insights into the nature and extent of tampering.

8. Take appropriate actions based on FakeShield’s detection and localization results, such as reporting false content, enhancing security measures, or conducting further investigations.