America’s unreported economy: measuring the size, growth and determinants of income tax evasion in the U.S.

Abstract This study empirically investigates the extent of noncompliance with the tax code and examines the determinants of federal income tax evasion in the U.S. Employing a refined version of Feige’s (1986; 1989) General Currency Ratio (GCR) model to estimate a time series of unreported income as our measure of tax evasion, we find that 18-23 % of total reportable income may not properly be reported to the IRS. This gives rise to a 2009 “tax gap” in the range of $390-$537 billion. As regards the determinants of tax noncompliance, we find that federal income tax evasion is an increasing function of the average effective federal income tax rate, the unemployment rate, the nominal interest rate, and per capita real GDP, and a decreasing function of the IRS audit rate. Despite important refinements of the traditional currency ratio approach for estimating the aggregate size and growth of unreported economies, we conclude that the sensitivity of the results to different benchmarks, imperfect data sources and alternative specifying assumptions precludes obtaining results of sufficient accuracy and reliability to serve as effective policy guides.Unreported economy; Underground economy; tax evasion; tax gap; noncompliance; income tax evasion; currency demand approach, currency ratio models

State of Utah v. Daniel L. Carter : Brief of Appellant

Appeal from judgments of conviction of Tax Evasion, a second degree felony, in violation of Utah Code Ann. § 76-8-1101(l)(d) (Supp. 2003), and Tax Evasion, a third degree felony, in violation of Utah Code Ann. § 76-8-1101(l)(c) (Supp. 2003), in the Third Judicial District Court, in and for Salt Lake County, State of Utah, the Honorable Leslie A. Lewis, presiding

The State of Utah v. Thomas Howard Smith : Brief of Appellant

Appeal from an order following judgment of conviction for tax evasion, a third degree felony offense in violation of Utah Code Ann. § 76-8-1101(1) (1995), and tax evasion, a second degree felony offense in violation of Utah Code Ann. § 76-8-1101(1) (1995), in the Third Judicial District Court in and for Salt Lake County, State of Utah, the Honorable Raymond S. Uno, Judge, presiding

The State of Utah v. Thomas Howard Smith : Brief of Appellant

Appeal from a judgment of conviction for tax evasion, a second degree felony offense in violation of Utah Code Ann. § 76-8-1101(l)(b) (1995), and tax evasion, a third degree felony offense in violation of Utah Code Ann. § 76-8-1101(1 )(c) (1995), in the Third Judicial District Court in and for Salt Lake County, State of Utah, the Honorable Raymond S. Uno, Judge, presiding

Tax Return Preparers and Tax Evasion

The IRS has determined that the largest amount of tax evasion is associated with a relatively small percentage of returns prepared by tax practitioners. Tax practitioners can generally serve in three roles—to assist aggressive tax planning and evasion, to act as agents for the IRS and enforce the tax code, or simply be expensive outlets for tax return preparation. Do the distributional statistics lead to the conclusion that tax practitioners cause rather than divert additional tax evasion? The purpose of this paper is to address the causal connection between return preparation choice and evasion. We find that the return characteristics for those seeking practitioners are associated with an increased opportunity for tax evasion. But our analysis also shows that tax practitioners actually lower tax evasion beyond what it would be if an individual had sought another means of preparation, such as self-preparation

Security Evaluation of Support Vector Machines in Adversarial Environments

Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering. However, if SVMs are to be incorporated in real-world security systems, they must be able to cope with attack patterns that can either mislead the learning algorithm (poisoning), evade detection (evasion), or gain information about their internal parameters (privacy breaches). The main contributions of this chapter are twofold. First, we introduce a formal general framework for the empirical evaluation of the security of machine-learning systems. Second, according to our framework, we demonstrate the feasibility of evasion, poisoning and privacy attacks against SVMs in real-world security problems. For each attack technique, we evaluate its impact and discuss whether (and how) it can be countered through an adversary-aware design of SVMs. Our experiments are easily reproducible thanks to open-source code that we have made available, together with all the employed datasets, on a public repository.Comment: 47 pages, 9 figures; chapter accepted into book 'Support Vector Machine Applications

An analysis of malware evasion techniques against modern AV engines

This research empirically tested the response of antivirus applications to binaries that use virus-like evasion techniques. In order to achieve this, a number of binaries are processed using a number of evasion methods and are then deployed against several antivirus engines. The research also documents the process of setting up an environment for testing antivirus engines, including building the evasion techniques used in the tests. The results of the empirical tests illustrate that an attacker can evade multiple antivirus engines without much effort using well-known evasion techniques. Furthermore, some antivirus engines may respond to the occurrence of an evasion technique instead of the presence of any malicious code. In practical terms, this shows that while antivirus applications are useful for protecting against known threats, their effectiveness against unknown or modified threats is limited

Untargeted Code Authorship Evasion with Seq2Seq Transformation

Code authorship attribution is the problem of identifying authors of programming language codes through the stylistic features in their codes, a topic that recently witnessed significant interest with outstanding performance. In this work, we present SCAE, a code authorship obfuscation technique that leverages a Seq2Seq code transformer called StructCoder. SCAE customizes StructCoder, a system designed initially for function-level code translation from one language to another (e.g., Java to C#), using transfer learning. SCAE improved the efficiency at a slight accuracy degradation compared to existing work. We also reduced the processing time by about 68% while maintaining an 85% transformation success rate and up to 95.77% evasion success rate in the untargeted setting.Comment: 9 pages, 1 figure, 5 table