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Dataset distillation, intended to compress data while preserving model performance, actually leaks sensitive information about the original training data and model architecture.
Current image watermarks are easily bypassed by pixel-wise reconstruction, raising serious questions about their effectiveness as a defense against misuse of machine-generated images.