Interval Centroid Based Flow Watermarking Technique for Anonymous Communication Traceback
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
基于扩频的流水印通过扩频技术对水印信号进行编码,将其嵌入特定通信流中以确认网络主体间的通信关系,可以有效地对匿名滥用进行追踪.流水印的实施分为编码、调制、解调、解码等步骤.其中,水印载体的选择尤为重要,关系到水印的健壮性和隐秘性.已有扩频流水印方案选用流速率作为水印载体,由于大部分匿名通信应用,如Web 浏览、即时通信、远程登录等均产生交互式流量,其速率是非稳定的,因而以流速率作为水印载体具有很大的局限性.此外,目前已存在多种针对此类水印隐秘性的攻击技术,降低了追踪的效果.在扩频流水印模型的基础上,引入与特定流无关的基于时隙质心的水印载体,提出一种新型流水印技术.理论分析与实验结果表明,这种新型流水印能够适用于对交互式与非交互式流量的追踪,有着更为广泛的适用性.此外,新型流水印能够有效抵抗现有攻击,保证追踪的隐秘性.;The spread spectrum based flow watermarking, which can be used to trace anonymity abuses effectively, applies spread spectrum technique to encode watermark signals and embeds them into suspect flows. This serves to confirm the communication relationship among network users. The implementation of watermarking can be divided into four phases: Signal encoding, flow modulation, flow demodulation and signal decoding. It is important to choose the right watermark carrier that determines the robustness and invisibility of watermarking techniques. Since most applications using anonymous communication, such as Web browsing, instant message and remote login generate interactive traffic with unstable traffic rate, existing spread spectrum based flow watermarking adopting traffic rate as its carrier has big limitations. Furthermore, there exist some attacks against the invisibility of this watermarking technique, destroying the traceback effect. Based on the spread spectrum flow marking model, this paper proposes a novel flow watermarking technique that adopts interval centroid as its watermark carrier, which is insensitive to different types of flows. The theoretical analysis and experimental results show that this flow watermarking technique is appropriate for both interactive and non-interactive traffic, and can resist most existing attacks against flow watermarking.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it