Neural Speech Codecs face a fundamental trade-off at low bitrates: preserving acoustic fidelity often compromises semantic richness. To address this, we introduce SACodec, a novel codec built upon an asymmetric dual-quantizer that employs our proposed Semantic Anchoring mechanism. This design strategically decouples the quantization of Semantic and Acoustic details. The semantic anchoring is achieved via a lightweight projector that aligns acoustic features with a frozen, large-scale mHuBERT codebook, injecting linguistic priors while guaranteeing full codebook utilization. Sequentially, for acoustic details, a residual activation module with SimVQ enables a single-layer quantizer (acoustic path) to faithfully recover fine-grained information. At just 1.5 kbps, SACodec establishes a new state of the art by excelling in both fidelity and semantics: subjective listening tests confirm that its reconstruction quality is perceptually highly comparable to ground-truth audio, while its tokens demonstrate substantially improved semantic richness in downstream tasks. This work suggests that assigning specialized semantic quantizers to distinct information streams offers an effective path to reconcile the long-standing trade-off between fidelity, semantics, and modeling simplicity in low-bitrate speech tokenization.

Diffusion models has threatened image authenticity by enabling highly realistic fakes. Proactive defense offers protection by adding a "protective layer" that resists such manipulation. However, current proactive defenses mainly focus on text-guided editing but are less effective for the more challenging exemplar-guided tasks. To bridge this gap, we propose Proactive Spatial-Frequency Defense (PSFD), a novel proactive defense for exemplar-guided image editing. PSFD leverages adversarial attack to add subtle perturbations to make images immune to editing. We apply protections in both the frequency and spatial domains. Spatial perturbation disrupts feature extraction by forcing visual encoders to map the image to "bad" representations. Frequency perturbation tweaks the high-frequency components to distort the image’s texture information. We design two optimization strategies: PSFD-U that aims to generate maximal variation and PSFD-T that seeks to achieve specific editing styles. Extensive experiments on MS-COCO and ImageNet demonstrate PSFD’s strong defensive capabilities and transferability.