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Apple Reference Image: A New Approach for Verified Photography

Apple introduces Reference Image, hardware-backed verifiable photography on iPhone 18 Pro using sensor signing and Private Cloud Compute to counter AI-generated fakes.

Apple announced Reference Image, an opt-in camera mode debuting on the main sensor of iPhone 18 Pro and iPhone 18 Pro Max that produces securely timestamped, verifiable photographs. The design splits into two phases: a secure digital negative created by cryptographically signing pixel data at the sensor immediately after capture (preventing injection or tampering), then developing that negative into a reference image. Private Cloud Compute handles processing without exposing image contents to anyone, including Apple, and fraudulent reference images can be revoked without revealing the photographer's identity. Apple positions the system as stronger than C2PA-based approaches, which sign metadata after capture, are vulnerable to editing-chain compromise, and can tie images to a device or individual.

Hacker News · securitySep 16, 02:07 UTC · 1d agoResearch HN 25↑ · 18 comments

Third-Party Script Security: How Tags, Pixels, and Embeds Can Put Websites at Risk

Sucuri details how third-party tags, pixels, and embeds expand website attack surface, letting compromised vendors read data and alter pages.

The article explains that third-party scripts used for analytics, ads, chat, payments, and video can read page content, collect visitor data, and change what users see. A compromise of the vendor, account, or integration configuration can affect every page loading the script. It recommends inventorying scripts, understanding their purpose and ownership, and detecting unexpected changes rather than removing useful integrations.

Sucuri BlogAug 24, 22:31 UTC · 23d agoResearch

DRIFT: Removing Diffusion Watermarks by Deflecting the Generative Trajectory

Introduces DRIFT, a black-box attack removing diffusion watermarks by deflecting generative trajectories, achieving 98-100% success across nine watermarking schemes.

Researchers propose DRIFT, a black-box watermark removal attack combining partial forward diffusion with stochastic reverse resampling to break trajectory-dependent verification. The paper derives information-theoretic and Wasserstein source-dependence bounds and shows the first verifier-rejected rung is least distorted among rejected rungs. Across nine watermarks spanning three paradigms, DRIFT achieves 98-100% attack success with the best image quality among compared attacks, without secret keys, verifier internals, or per-image gradient optimization.

arXiv cs.CRSep 8, 03:54 UTC · 9d agoResearch