在1KB内压缩人脸图像仍能保留身份信息，关键取决于具体字节预算。
Toward Sub-1 kB Identity-Preserving Face Compression: A Benchmark of Codecs, a Custom Learned Codec, and Studies of Resolution, Demographic Fairness, Recompression, and Adversarial Robustness
- 针对不同字节预算设计专用压缩算法，精准控制编码精度。
- 512字节时新算法误拒率仅6.9%，远优于传统格式的24%~98%。
- 适用于身份证、智能卡等低带宽身份验证场景，强调实际部署适配性。
在硬性1KB以下存储人脸图像（如身份证件、智能卡生物识别和带宽受限验证）时，需在丢弃大部分信号的同时保留人脸匹配器所依赖的身份信息。通用编码器优化像素保真度，而非驱动验证的嵌入距离，因此在1024字节及以下预算下，何种编码器、分辨率与设置最能保持身份一致性尚不明确。本文在两个数据集（受控的Color FERET和真实场景的AI-Solutions-KK）上对十种通用与人脸专用编码器进行基准测试，涵盖四种锚点人脸匹配器，并通过十四种基于ViT和CNN的模型验证排名具有主干网络无关性。随后训练了一个定制化身份保全编码器，通过冻结增益表的二分搜索精确达到字节预算。进一步开展四项研究：分辨率影响、人群公平性、重压缩性能与无框对抗鲁棒性。结果表明，1KB以内身份保留可行，但最佳选择完全依赖预算。在1024字节、112像素工作分辨率下，问题接近解决：现代编码器在ArcFace锚点上使Color FERET等错误率低于0.35%。而在512字节时，格局重排：AVIF、HEIF、JPEG XL及旧版JPEG在FMR=1e-4时误非匹配率升至28%~98%，而WebP、JPEG-AI及本研究提出的字节精确编码器表现稳定，其中在野外场景下，WebP为24.3%，本研究准确版本仅为6.9%。这一重排——而非1024字节排名——才是实际部署的关键：1KB下的最优编码器未必适合512字节环境。
原文摘要 · Abstract (English)
Storing face images under a hard sub-kilobyte budget, as required for identity documents, smart-card biometrics and bandwidth-constrained verification, forces a codec to discard most of the signal while keeping what a face matcher actually reads: identity. Generic codecs optimize pixel fidelity, not the embedding distances that drive verification, so which codec, resolution and setting best preserve identity at 1024 bytes or less, and how that degrades at 512, is unclear. We benchmark ten general and face-specific codecs across resolutions, byte budgets, two datasets (controlled Color FERET, in-the-wild AI-Solutions-KK) and four anchor face matchers, with a fourteen-model ViT and CNN roster confirming the ranking is backbone-invariant. We then train a custom identity-preserving codec that hits the byte budget exactly via binary search over a frozen gain table, and run four studies: resolution, demographic fairness, recompression, and no-box adversarial robustness. Sub-kilobyte identity preservation is feasible, but which codec to deploy depends entirely on the budget. At 1024 bytes and the 112 px working resolution the problem is close to solved: modern codecs hold Color FERET equal-error rate under 0.35 percent on the ArcFace anchor. At 512 bytes the field re-sorts: AVIF, HEIF, JPEG XL and legacy JPEG collapse to 28 to 98 percent false-non-match rate at FMR 1e-4, while WebP, JPEG-AI and our byte-budgeted learned codecs stay out of that band, with 24.3 percent for WebP against 6.9 percent for our accurate variant in the wild. That re-sort, not the 1024-byte ranking, is the operational result: a codec chosen at 1 kB is not the codec to deploy at half that.
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