联合国报告恐怖组织利用门罗币募捐,监管的「紧箍咒」更紧了?

深潮Published on 2024-08-06Last updated on 2024-08-06

ISIL 等恐怖组织在其宣传的电子杂志中嵌入二维码,以门罗币等加密货币募捐。

撰文:Jahnu Jagtap,Cryptotimes

编译:Felix,PANews

随着恐怖组织适应日益数字化的世界,寻找新的筹集和转移资金的方式,全球反恐斗争正面临新的挑战。根据联合国安理会监察小组最近的调查结果,臭名昭著的恐怖组织 ISIL 从其长期信任的「哈瓦拉」(注:指一种非正式的资金流动方式,一般通过非授权经销商进行交易。此类交易不在印度中央银行的监管范围内,因此无法追溯资金源头)「迁移」到加密货币。

该报告指出,像 ISIL 和基地组织这样的恐怖组织正放弃传统的募资方式,如哈瓦拉、绑架和勒索,现在更倾向于使用「匿名性更强」的加密货币(以门罗币为代表),并在其宣传的电子杂志中嵌入二维码募捐。

这些组织甚至设置了「清真规范」(清真在伊斯兰教的社会中被译为合法),以推广其意识形态和运作。报告称,这些组织在 Telegram messenger 应用程序上设置了两个专门的加密频道,分别为 CryptoHalal 和 Umma Crypto,指导支持者获取和使用特定的数字货币,并接受根据「初步伊斯兰教法评估」批准的加密捐款。

例如,ISIL-K 利用隐私币的匿名性,使用链接到门罗币钱包的二维码发起募捐活动。尽管一些加密交易所将门罗币下架,但恐怖组织对门罗币的使用仍在增加,这使得当局很难追踪资金流向。

2020 年 8 月,美国政府查封了 300 多个加密账户、多个网站及 Facebook 页面,据称这些账户属于基地组织、ISIS 和哈马斯军事组织的成员。

值得注意的是,ISIL 对数字平台的使用范围不断扩大,引起了会员国的日益担忧。各种加密货币交易所、游戏平台、电子钱包和稳定币都用于筹集和转移资金。一个会员国指出,虽然使用现金快递和哈瓦拉汇款是资金转移到冲突地区的首选,但 ISIL 已有意转向加密货币和在线支付系统。随着电子钱包、预付手机卡销售和加密货币等数字方式的日益普及,预计此现象将变得更加普遍和重要。

由于能够混淆交易细节,门罗币等隐私币已成为恐怖主义融资的首选媒介。联合国报告强调了监控这些交易的难度,因为它们提供了传统金融系统无法比拟的匿名性。ISIL 及其分支利用这些特征进行筹款活动,确保他们的金融活动不被当局发现。

报告还强调了恐怖主义融资网络的复杂性。ISIL 的附属机构,特别是在非洲的附属机构,为该组织的资金募集做了重大贡献。这些附属机构通常依赖非正式渠道,使其不易受到干扰。例如,ISIL-K 在 2023 年募集了 250 万美元,其中一些可能与特定袭击有关,突显了这些组织构成的持续威胁。

联合国安理会监察小组的这份措辞严厉的报告,势必会让包括美国、英国、欧洲和印度在内的几个国家重视。近日,美国财政部金融犯罪执法网络(FinCEN)发出警告,敦促金融机构监控可能与恐怖组织哈马斯有关的加密货币交易。

此外,这份报告的影响也将波及加密行业。因为鹰派安全机构可能会加强与可疑活动有关资产流动监控。

Related Reads

Open Systems Will Ultimately Prevail: Why Ethereum Is the Next Linux?

The article "Open Systems Will Ultimately Prevail: Why Ethereum Is the Next Linux?" argues that Ethereum, like Linux before it, will triumph over closed, proprietary systems in finance due to its open, permissionless, and credibly neutral nature. It draws a historical parallel: just as the open internet defeated corporate private networks and Linux outcompeted proprietary Unix systems, open financial infrastructure like Ethereum will surpass private blockchains. The core advantage lies in the "bazaar" development model (as described in Eric Raymond's "The Cathedral and the Bazaar"), where decentralized, permissionless innovation by a global community of developers outpaces the controlled "cathedral" approach of centralized entities. This model fosters rapid innovation, as seen with Ethereum standards like ERC-20 and applications like Uniswap, which were built without needing permission. Ethereum's key, irreplicable strength is its credible neutrality: transparent, equally applicable, immutable rules that allow anyone to participate. This ensures sovereign independence, meaning no single entity (company, government) can control or change its core rules—a critical feature for global financial infrastructure. In contrast, private blockchains and consortium chains (like SWIFT or various bank-led projects) suffer from platform risk, central control, and an inability to attract broad developer ecosystems, leading to frequent failures. The article notes that major institutions (e.g., BlackRock, JPMorgan, Coinbase, Robinhood) are already building on Ethereum or its Layer 2 networks, recognizing its security, developer ecosystem, and network effects. While critics argue finance requires accountable, controlled systems, the response is that compliance (KYC, regulations) can be built at the application layer on top of a neutral settlement layer like Ethereum, just as secure commerce was built on the open internet via HTTPS. Ultimately, the thesis is that attempting to build walled-garden, proprietary financial networks is a flawed strategy that stifles innovation. The winning approach is to build applications on top of open, credibly neutral infrastructure like Ethereum, which is poised to become the foundational settlement layer for global finance.

Foresight News5m ago

Open Systems Will Ultimately Prevail: Why Ethereum Is the Next Linux?

Foresight News5m ago

The Computing Power Dilemma in the Sino-US AI Rivalry

The Sino-US AI rivalry faces a fundamental bottleneck: the widening compute power gap. While Chinese AI chip companies have seen investment surges, their current focus remains largely on the less demanding inference market. The real challenge lies in the high-end training chip sector, crucial for developing cutting-edge large language models (LLMs), where Nvidia holds a near-monopoly. The compute disparity is stark. US tech giants like Meta, Google, and xAI command massive GPU clusters, enabling them to train trillion-parameter models rapidly. Estimates suggest US data center count and total compute capacity significantly outstrip China's. This "brute force" advantage allows for faster model iteration and exploration of larger parameter scales, with top US models reportedly leading their Chinese counterparts by 8 to 15 months. Chinese alternatives, such as Huawei's Ascend and others from companies like Moore Thread and Biren, are emerging. They show promise in inference and some training scenarios, closing the performance gap with mid-range Nvidia products. However, the core hurdle extends beyond raw chip performance to the entrenched software ecosystem, exemplified by Nvidia's CUDA platform. The path forward involves "walking on two legs": navigating import restrictions while heavily investing in the domestic chip industry. Though still in a catch-up phase, China's vast market, talent pool, and capital are fostering progress. The ultimate test is whether Chinese firms can build a competitive hardware-software ecosystem to power the next generation of AI.

marsbit12m ago

The Computing Power Dilemma in the Sino-US AI Rivalry

marsbit12m ago

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

KaiMing He's team introduces **MiniT2I**, a minimalist text-to-image (T2I) model that challenges the complexity of mainstream approaches. It eliminates components commonly considered essential: the VAE encoder-decoder, AdaLN conditioning mechanisms, auxiliary losses, private training data, and post-training alignment stages like RL/DPO. Instead, it uses a pure flow-matching objective trained directly on RGB pixels. The model employs a simplified **MM-JiT** Transformer architecture. It removes AdaLN blocks for conditioning and instead prepends two lightweight text adapter blocks to a standard pre-norm Transformer, allowing frozen T5 text features to adapt to the denoiser. Training follows a two-stage, LLM-like paradigm using only public datasets: pre-training on LLaVA-recaptioned CC12M for coverage, followed by fine-tuning on ~120k high-quality image-text pairs. With just 258M parameters (B/16), MiniT2I achieves competitive scores (0.87 on GenEval, 84.2 on DPG-Bench), outperforming larger pixel-space models. Scaling to 912M parameters (L/16) yields results comparable to SD3-Medium (~2B parameters) in style, composition, and imagination, though it lags in text rendering and named entities due to public data limitations. Key advantages include lower computational cost (~570 GFLOPs vs. ~1379 for latent models) and architectural simplicity. Acknowledged limitations include patch boundary artifacts in pixel space, side effects of high CFG scales, resolution ceilings for sequences longer than 1024 tokens, and the aforementioned data bottlenecks. The work demonstrates that high-performance T2I generation is possible with a radically simplified, publicly reproducible baseline.

marsbit16m ago

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

marsbit16m ago

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

The insurance industry, long a stable "ballast" in the economy, may face a significant challenge from the rise of prediction markets, which are beginning to function as a new form of risk hedging and insurance. Platforms like Kalshi and Polymarket are demonstrating their utility in areas traditionally dominated by insurers. Examples include Kalshi's partnership with sports insurance broker Game Point Capital to offer more cost-effective hedging for NBA team performance bonuses, and Polymarket's collaboration with real estate platform Parcl, allowing users to hedge against housing price fluctuations in major US cities. A New York bar also used Kalshi to hedge a marketing promotion tied to an NBA game outcome, highlighting prediction markets' potential for small business risk management. These markets offer advantages over traditional insurance and sports betting in transparency, liquidity, and flexibility. They allow information monetization across a wider range of events, act as neutral platforms rather than direct counterparties, and provide clearer pricing. A historical precedent is the "Mattress Mack" marketing campaigns, which used sports betting for large-scale customer refunds, but prediction markets offer a more systematic and accessible model. Experts like SIG CEO Jeff Yass see their potential for efficient, parameter-based risk sharing, such as for weather-related property damage. However, challenges remain, including liquidity issues, unclear regulatory boundaries, and potential manipulation of event outcomes. Despite these hurdles, prediction markets represent a growing competitive force for both traditional gambling platforms and segments of the insurance industry.

marsbit17m ago

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

marsbit17m ago

Trading

Spot
Futures
活动图片