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A groundbreaking watermarking method that not only traces LLM-generated content but also detects tampering with unprecedented accuracy.
Even without architectural modifications, a new gradient inversion attack, ARES, can reconstruct high-fidelity training samples in federated learning, exposing a significant privacy risk.
