AI Antispoofing

Generative AI is blurring the lines between creativity and legality. Who owns the rights to AI-generated content? Courts say no to AI copyrights, pushing us to redefine intellectual property laws. Are we ready to navigate this complex landscape? #AI #Copyright #LegalTech #Innovation

The accuracy of AI text detection tools varies significantly across different studies and tools. Here are some key findings from recent research and evaluations: 1. Originality AI: This tool has been highlighted for its high accuracy in several studies. It achieved 85% accuracy on a base dataset and performed exceptionally well in adversarial settings, particularly in identifying paraphrased content with 96.7% accuracy. However, in another independent test, it achieved just 76% accuracy. 2. Copyleaks: A study published on the Cornell Tech-owned arXiv declared Copyleaks the most accurate AI-generated text detector among 8 tested tools. Another study ranked it in the middle among 12 tools with 66% accuracy. 3. Sapling: In a comparative study of 12 different AI detectors, Sapling's AI detector scored 68% accuracy, placing it in the middle range of the tools tested. 4. Winston AI: Claims an impressive accuracy rate of 99.98% in detecting AI-generated content from various models. However, one review found that Winston AI only detected one out of seven AI-generated text samples. Another review noted that Winston AI flagged a human-written school essay as only 43% human-generated. 5-6. Scribbr and QuillBot: These tools were noted for their high accuracy among free options, both scoring 78% in tests. The premium version of Scribbr showed 84% accuracy. #aidetector #ai #textdetector #textgeneration #review

With the advent of Large Language Models, new terminology is emerging as well. One such term is "data poisoning." What exactly does it mean? Definitions and examples are clarified in our article. #data #LLM #cybersecurity

What are prompt injection attacks on LLMs? Check out our latest article with notable examples #llm #injection

#Deepfakes in 2024 - a Summary of #Trends in #KYC

Machine-generated texts become hard to detect in the era of GenerativeAI, which can give rise to an influx of malicious activities on social media. Check out our latest overview of text generation methods and detection techniques and models #chatbots #generatedtext #generativeai #deepfakedetection

Fundamentally, all Generative AI models can be subject to worm attacks, including pictures, audio, code, and video generators. Large Language Models (LLMs) are the first targets to select due to their immense popularity and also the versatility of output they can produce: emails, software code, dialogue, and so on. These unique abilities make LLMs popular among developers who seek to integrate them into their products. GenAI worms focus on weaknesses that are inseparable from the current GenAI models. These vulnerable spots include the prompt-and-input component and the monolith nature of today’s Generative AI, allowing a wildfire proliferation of malware throughout the system. #generativeai #worms #ai #cybersecurity

ChatGPT might inadvertently reveal sensitive data. Here are some real-life examples and findings from recent studies: 1. A team of Google researchers discovered a method to extract personal information from ChatGPT by exploiting its training data. They managed to retrieve over 10,000 unique verbatim memorized training examples, including names, email addresses, and phone numbers, with just $200 USD worth of queries. This suggests that with larger budgets, adversaries could potentially extract even more sensitive data. The attack involved prompting ChatGPT to repeat certain words indefinitely, causing it to diverge from its intended responses and reveal memorized information from its training data. 2. Researchers at Indiana University used ChatGPT's model to extract contact information for more than 30 New York Times employees. They found that while the model's recall was not perfect, with some personal email addresses being incorrect, 80% of the work addresses returned were accurate. This experiment highlighted the potential for ChatGPT and similar generative AI tools to reveal sensitive personal information with slight modifications to the querying process. The researchers bypassed the model's restrictions on responding to privacy-related queries by not working directly with ChatGPT’s standard public interface but rather with its application programming interface (API), which allowed them to fine-tune the model and foil some of the built-in defenses The extraction of personal information through vulnerabilities in models like ChatGPT, highlights the urgent need for robust privacy safeguards and ethical considerations in the development and deployment of LLMs #chatgpt #ai #pii #privacy

Thrilled to share some groundbreaking news that's set to reshape the landscape of artificial intelligence in Europe: The AI Act has officially been approved by Parliament last week! #AIAct #ArtificialIntelligence #Innovation #EthicalAI

Here are some of the new trends and methods of deepfake attacks in KYC being used by fraudsters: 1. Deepfake Identity Fraud: There has been a tenfold increase in deepfake identity fraud from 2022 to early 2023, indicating a significant rise in the use of deepfakes to create fake identities for fraudulent purposes. 2. Video Call Verification Vulnerabilities: Deepfake technology is being used to spoof identity documents and faces during video call verifications, which are a common method used in KYC processes. This has raised concerns about the vulnerability of such methods to deepfake attacks. 3. Crypto Sector Targeting: The cryptocurrency sector has been particularly affected, accounting for 88% of all deepfake cases detected in 2023. This is followed by the fintech sector, which accounts for 8% of the cases. 4. Regional Growth Rates: The growth rate of deepfake use has varied by region, with North America experiencing a 1740% increase and the Asia-Pacific region seeing a 1530% surge from 2022 to 20231. 5. Consumer Concerns: A significant portion of consumers, 90%, have expressed concerns about deepfake attacks, and 70% could not confidently differentiate between a real voice and a cloned one. 6. Financial Losses: Among those surveyed, 10% had received a message from an AI voice clone, and 77% of these individuals lost money as a result. 7. Cost of Deepfakes: The cost of purchasing ready-made deepfakes varied between $300 to $20,000 per minute in 2023, depending on the complexity, quality, and the fame of the impersonated person. 8. Accessibility of Crime-as-a-Service Tools: The growing affordability and accessibility of Crime-as-a-Service tools, particularly in the realm of speech and video synthesis, are making it easier for even low-skilled criminals to carry out sophisticated attacks. *The image is deepfake generated example #deepfakes #trends #cybersecurity #kyc