9 research outputs found
PredNet and Predictive Coding: A Critical Review
PredNet, a deep predictive coding network developed by Lotter et al.,
combines a biologically inspired architecture based on the propagation of
prediction error with self-supervised representation learning in video. While
the architecture has drawn a lot of attention and various extensions of the
model exist, there is a lack of a critical analysis. We fill in the gap by
evaluating PredNet both as an implementation of the predictive coding theory
and as a self-supervised video prediction model using a challenging video
action classification dataset. We design an extended model to test if
conditioning future frame predictions on the action class of the video improves
the model performance. We show that PredNet does not yet completely follow the
principles of predictive coding. The proposed top-down conditioning leads to a
performance gain on synthetic data, but does not scale up to the more complex
real-world action classification dataset. Our analysis is aimed at guiding
future research on similar architectures based on the predictive coding theory
IDTraffickers:An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements
Human trafficking (HT) is a pervasive global issue affecting vulnerable individuals, violating their fundamental human rights. Investigations reveal that a significant number of HT cases are associated with online advertisements (ads), particularly in escort markets. Consequently, identifying and connecting HT vendors has become increasingly challenging for Law Enforcement Agencies (LEAs). To address this issue, we introduce IDTraffickers, an extensive dataset consisting of 87,595 text ads and 5,244 vendor labels to enable the verification and identification of potential HT vendors on online escort markets. To establish a benchmark for authorship identification, we train a DeCLUTR-small model, achieving a macro-F1 score of 0.8656 in a closed-set classification environment. Next, we leverage the style representations extracted from the trained classifier to conduct authorship verification, resulting in a mean r-precision score of 0.8852 in an open-set ranking environment. Finally, to encourage further research and ensure responsible data sharing, we plan to release IDTraffickers for the authorship attribution task to researchers under specific conditions, considering the sensitive nature of the data. We believe that the availability of our dataset and benchmarks will empower future researchers to utilize our findings, thereby facilitating the effective linkage of escort ads and the development of more robust approaches for identifying HT indicators
IDTraffickers:An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements
Human trafficking (HT) is a pervasive global issue affecting vulnerable individuals, violating their fundamental human rights. Investigations reveal that a significant number of HT cases are associated with online advertisements (ads), particularly in escort markets. Consequently, identifying and connecting HT vendors has become increasingly challenging for Law Enforcement Agencies (LEAs). To address this issue, we introduce IDTraffickers, an extensive dataset consisting of 87,595 text ads and 5,244 vendor labels to enable the verification and identification of potential HT vendors on online escort markets. To establish a benchmark for authorship identification, we train a DeCLUTR-small model, achieving a macro-F1 score of 0.8656 in a closed-set classification environment. Next, we leverage the style representations extracted from the trained classifier to conduct authorship verification, resulting in a mean r-precision score of 0.8852 in an open-set ranking environment. Finally, to encourage further research and ensure responsible data sharing, we plan to release IDTraffickers for the authorship attribution task to researchers under specific conditions, considering the sensitive nature of the data. We believe that the availability of our dataset and benchmarks will empower future researchers to utilize our findings, thereby facilitating the effective linkage of escort ads and the development of more robust approaches for identifying HT indicators
VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets
The anonymity on the Darknet allows vendors to stay undetected by using
multiple vendor aliases or frequently migrating between markets. Consequently,
illegal markets and their connections are challenging to uncover on the
Darknet. To identify relationships between illegal markets and their vendors,
we propose VendorLink, an NLP-based approach that examines writing patterns to
verify, identify, and link unique vendor accounts across text advertisements
(ads) on seven public Darknet markets. In contrast to existing literature,
VendorLink utilizes the strength of supervised pre-training to perform
closed-set vendor verification, open-set vendor identification, and
low-resource market adaption tasks. Through VendorLink, we uncover (i) 15
migrants and 71 potential aliases in the Alphabay-Dreams-Silk dataset, (ii) 17
migrants and 3 potential aliases in the Valhalla-Berlusconi dataset, and (iii)
75 migrants and 10 potential aliases in the Traderoute-Agora dataset.
Altogether, our approach can help Law Enforcement Agencies (LEA) make more
informed decisions by verifying and identifying migrating vendors and their
potential aliases on existing and Low-Resource (LR) emerging Darknet markets
Tx-ray: Quantifying and explaining model-knowledge transfer in (un-) supervised NLP
While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify model knowledge transfer during (un-) supervised training. Thus, for TX-Ray, we modify the established computer vision explainability principle of ‘visualizing preferred inputs of neurons’ to make it usable for both NLP and for transfer analysis. This allows one to analyze, track and quantify how self-or supervised NLP models first build knowledge abstractions in pretraining (1), andthen transfer abstractions to a new domain (2), or adapt them during supervised finetuning (3)–see Fig. 1. TX-Ray expresses neurons as feature preference distributions to quantify fine-grained knowledge transfer or adaptation and guide human analysis. We find that, similar to Lottery Ticket based pruning, TX-Ray based pruning can improve test set generalization and that it can reveal how early stages of self-supervision automatically learn linguistic abstractions like parts-of-speech
VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets
The anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets. Consequently, illegal markets and their connections are challenging to uncover on the Darknet. To identify relationships between illegal markets and their vendors, we propose VendorLink, an NLP-based approach that examines writing patterns to verify, identify, and link unique vendor accounts across text advertisements (ads) on seven public Darknet markets. In contrast to existing literature, VendorLink utilizes the strength of supervised pre-training to perform closed-set vendor verification, open-set vendor identification, and low-resource market adaption tasks. Through VendorLink, we uncover (i) 15 migrants and 71 potential aliases in the Alphabay-Dreams-Silk dataset, (ii) 17 migrants and 3 potential aliases in the Valhalla-Berlusconi dataset, and (iii) 75 migrants and 10 potential aliases in the Traderoute-Agora dataset. Altogether, our approach can help Law Enforcement Agencies (LEA) make more informed decisions by verifying and identifying migrating vendors and their potential aliases on existing and Low-Resource (LR) emerging Darknet markets