Written by 6:58 pm AI Threat, Deepfakes

### The Ineffectiveness of Digital Watermarks in Combating Deepfakes and AI Scams

The ease of removing Samsung’s new watermarks for AI images points to a bigger problem.

Hello and welcome to Eye on AI.

There is a growing realization that we are entering an era of increasing artificiality, largely facilitated by generative AI technology. Recent reports revealed instances of robocalls in New Hampshire using a voice resembling President Joe Biden, likely generated by voice cloning AI, influencing voters in the Republican primary. With open primaries in New Hampshire, Democrats aimed to impact Donald Trump’s GOP nomination by supporting Nikki Hailey. While authorities investigate these robocalls, the government faces challenges in preventing AI-driven deceptive activities. Voice cloning poses a significant threat, especially with over 4 billion eligible voters globally, including in the upcoming U.S. presidential election, potentially leading to extensive election interference. Moreover, voice cloning techniques are exploited in scams where fraudsters impersonate individuals in distress to extort money. Financial scams involve impersonating CEOs to manipulate finance executives into making unauthorized payments, highlighting the risks associated with AI manipulation.

Despite these concerns, investors continue to show interest in synthetic media companies like ElevenLabs, with the London-based startup recently securing $80 million in funding from prominent venture capital firms. The rise of synthetic media raises questions about the authenticity and trustworthiness of digital content, emphasizing the need for robust solutions to combat misinformation and fraud.

Efforts to address these challenges include the concept of “digital watermarking” to verify content authenticity. However, recent developments, such as Samsung’s Galaxy S24 smartphone with AI editing tools, reveal limitations in watermarking effectiveness. While digital watermarks can be easily removed, metadata embedded in image files may offer a more resilient verification method. Companies like Adobe and Microsoft are exploring cryptographic standards like C2PA to enhance content authenticity, yet challenges remain in ensuring the integrity of digital content in the face of AI manipulation.

Getty Images CEO Craig Peters advocates for a layered approach to authenticity, combining metadata indicators with a global provenance standard to verify the legitimacy of AI-generated content. Initiatives like the Content Authenticity Initiative aim to establish trust in digital content through encrypted metadata and verifiable credentials. However, existing standards like C2PA have encountered flaws, underscoring the ongoing need for improved solutions to combat AI-driven misinformation.

As we navigate through the complexities of AI-driven deception, the urgency for innovative solutions to safeguard the integrity of digital content becomes increasingly apparent. The evolving landscape of synthetic media and AI manipulation underscores the critical importance of upholding authenticity and trust in an era dominated by technological advancements.


Sam Altman Explores Nvidia Rivalry: OpenAI’s Sam Altman is reportedly in discussions with Middle Eastern investors, SoftBank, and TSMC to establish an AI computing chipmaker that could challenge Nvidia’s dominance in GPU production. The potential involvement of influential investors and semiconductor manufacturers highlights the growing competition in the AI hardware market.

Cohere Seeks Major Funding: AI startup Cohere is in talks to raise up to $1 billion in venture funding, surpassing its previous valuation and signaling investors’ continued interest in AI startups. The funding round will serve as a test of market appetite for investing in AI technologies at increasingly higher valuations.

Generative AI Startups’ Valuations: Concerns arise over the valuation of generative AI startups like Anthropic, with questions raised about their profitability and long-term sustainability. The discrepancy in gross profit margins compared to traditional software companies raises doubts about the valuation metrics applied to AI startups and the potential risks for investors.

Fairly Trained AI Models: A new certification initiative, Fairly Trained, aims to certify AI models as ethically trained by ensuring compliance with copyright laws and licensing terms. The certification process involves detailed disclosure of training data sources and licensing agreements, providing transparency in the development of AI models.

Debate Over AI-Generated Literature: The awarding of a Japanese literary prize to a novel partially written with AI assistance sparks debate over the role of AI in creative endeavors. While some view AI integration as an innovative experiment, others raise concerns about the impact on traditional literary practices and fair competition.


Challenges in Machine Translation: The use of multi-way parallel translation datasets in training large language models poses challenges for low-resource languages, affecting translation quality and accessibility. Efforts to address this issue by creating better organic datasets for underrepresented languages are essential to improve machine translation accuracy and inclusivity.

Google DeepMind’s Geometry Breakthrough: Google DeepMind’s recent achievement in solving complex geometry problems at the level of human competitors signifies a significant advancement in AI capabilities. The system’s performance in mathematical problem-solving showcases the potential for AI to excel in diverse cognitive tasks.


Exploring AI Applications: Fortune delves into various AI developments, including advancements in self-driving technology, AI chatbot interactions, data governance, and the disruptive impact of generative AI on business services. Insights from industry leaders and experts shed light on the evolving landscape of AI technologies and their implications for diverse sectors.


Ethical Concerns in Facial Recognition: Wired highlights ethical concerns surrounding law enforcement’s use of facial recognition software, emphasizing the risks of relying on AI technologies for criminal investigations. The story underscores the need for regulatory frameworks to ensure responsible and transparent use of facial recognition tools in law enforcement practices.

As we navigate the complexities of AI-driven advancements and their societal implications, the critical importance of ethical AI development and responsible deployment becomes increasingly apparent. Stay tuned for more updates on the evolving landscape of artificial intelligence and its impact on various industries and sectors.

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Last modified: January 24, 2024
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