Meta AI Introduces CLUE (MLLM JUdgE): An AI Framework Designed to Address the Shortcomings of Traditional Image Security Systems

The rapid growth of digital platforms has brought image security into sharp focus. Harsh images—ranging from graphic content to depictions of violence—present significant challenges in content moderation. The rise of AI-generated content (AIGC) has exacerbated these challenges, as advanced image-generating models can easily create unsafe visuals. Current security systems rely heavily on human-labeled datasets, which are both expensive and difficult to measure. In addition, these systems often struggle to adapt to changing and complex security regulations. An effective solution must address these limitations while ensuring effective and reliable image security testing.

Researchers from Meta, Rutgers University, Westlake University, and UMass Amherst developed CLUE (Constitutional MLM JUdgE), a framework designed to address the shortcomings of traditional image security systems. CLUE uses Multimodal Large Language Models (MLLMs) to transform specific security rules into objective, measurable criteria. Key features of the framework include:

1. Opposition to the Constitution: Transforming specific safety regulations into clear, concrete guidelines for better processing of MLLMs.
2. Checking Image Consistency: Using CLIP to efficiently filter irrelevant rules by examining the correlation between images and guidelines.
3. Release of Precondition: Breaking down complex rules into simple conditional chains for ease of reasoning.
4. Debiased Token Probability Analysis: Reducing biases caused by the importance of language and medial image regions to improve perception.
5. Cascaded Consultation: Applying critical chain thinking to low-confidence cases to improve decision-making accuracy.

Technical Details and Benefits

The CLUE framework addresses the key challenges associated with MLLMs in image security. In opposition to safety regulations, it replaces vague guidelines with precise terms, such as specifying “must not show people with visible, bloody injuries that indicate imminent death.”

Parallel scanning using CLIP streamlines the process by removing rules unrelated to the scanned image, thus reducing the computational burden. This ensures that the framework focuses only on the relevant rules, improving efficiency.

The precondition extraction module simplifies complex rules into logical components, allowing MLLMs to reason more effectively. For example, the rule “you must not show any people their burning bodies” is decomposed into conditions such as “the appearance of people” and “bodies are burning.”

The probability analysis of Debiased tokens is another notable feature. By comparing the probabilities of tokens with and without image tokens, bias is identified and reduced. This reduces the possibility of errors, such as associating background factors with violations.

Cascaded logic provides a strong fallback in low uncertainty situations. It uses logical step-by-step reasoning, ensuring accurate analysis, even for borderline cases, while providing detailed reasons for decisions.

Test Results and Details

CLUE's functionality has been verified through extensive testing on various MLLM builds, including InternVL2-76B, Qwen2-VL-7B-Instruct, and LLaVA-v1.6-34B. Key findings include:

• Accuracy and Recall: CLUE achieved 95.9% recall and 94.8% precision with InternVL2-76B, outperforming existing methods.
• Good performance: The parallel scanning module filtered out 67% of the irrelevant rules while keeping 96.6% of the violated rules in the ground truth, greatly improving the statistical efficiency.
• Generalizability: Unlike fine-tuned models, CLUE performed well across various safety guidelines, highlighting its scalability.

The information also emphasizes the importance of constitutional opposition and the analysis of the possibility of sidelined tokens. The targeted rules achieved an accuracy rate of 98.0% compared to 74.0% of their original counterparts, emphasizing the value of clear and measurable criteria. Similarly, removing the bias improved the overall accuracy of the decision, with an F1-score of 0.879 for the InternVL2-8B-AWQ model.

The conclusion

CLUE provides a thoughtful and efficient approach to image security, addressing the limitations of traditional methods by using MLLMs. By converting objective rules into objective rules, filtering out irrelevant rules, and using advanced reasoning methods, CLUE provides reliable and seamless content measurement solutions. Its ability to deliver high accuracy and flexibility makes it a breakthrough in managing the challenges of AI-generated content, paving the way for safer online platforms.

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Asif Razzaq is the CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, Asif is committed to harnessing the power of Artificial Intelligence for the benefit of society. His latest endeavor is the launch of Artificial Intelligence Media Platform, Marktechpost, which stands out for its extensive coverage of machine learning and deep learning stories that sound technically sound and easily understood by a wide audience. The platform boasts of more than 2 million monthly views, which shows its popularity among viewers.