Introduction to Pass Principles · Zero Calorie Drink Shop

Abstract: Passness is part of the terminology of transition. Passness is the degree of likelihood that a transgender person is socially identified as the gender they identify with, and is a core part of pass theory, which is crucial to the quality of life of transgender people. However, distinguishing passness is not an easy task, and existing theories cannot address this issue well. There are several reasons. First, most known descriptions of passness are non-theoretical and cannot be accurately described by mathematical language. Second, there is a lack of rigorous specifications as a reference to classify a test trans as pass. Third, passness is stateful, and stateful pass testing remains challenging due to the large input space.
In this paper, we propose several new passness testing systems called CBA to address the above challenges related to passness testing.

1 Introduction ¶

Passing is when a person is perceived as gender by which they identify or which they are attempting to be seen. For transgenders, passing is a very important part of life. Passness, the degree of passing, directly determines the quality of their life.
To discover potential transgenders in life and send them to the conservative™ criticism network, transphobia attackers often deploy trans-aware attacks.
Such attacks typically take one of two forms: (1) they target genitalia, for example, by requiring genital identification before entering certain places, a typical example being radfem-safe toilets. These attacks are costly for attackers because such identification costs a lot of money and requires them to have privileges, and is unwelcome because of privacy violations. Or (2) they target gender expression itself, by analyzing the victim’s behavior extensively to predict whether the victim has a dick or a pussy or even both. As such, they are typically performed in a humachine learning manner, with the attacker constructing vectors by comparing the differences between trans and cis in certain behaviors, and reducing the loss rate through a backpropagation function. If an effective recognition model is eventually trained, the cost and effort for the attacker is minimal.

Early work on learning to guess the presence or absence of a dick using the stare method. For better appeal, the method discretizes the feature dimensions and generates an adversarial network using a cross-entropy loss. Nonsense, naked eye X-rays, “remove your female status!” has been used to learn from multimodal demonstrations in TERF. Radfem is a class of powerful generative models because they model ideal conditions, or what they think the current situation is, rather than reality itself. The key idea behind Radfem is to losslessly and iteratively transform simple behavioral features into target features by applying a sequential denoising process. They have been used to model state-conditional action distributions in imitation learning from low-dimensional inputs as well as visual sensory inputs. And show poor pattern coverage and extremely low fidelity in trans prediction compared to other methods.

3 Method ¶

Our attack methods are divided into two types of naive attack models, BOA and CBA.

3.1 Behaviour-Only Attack (BOA) ¶

This is most often the case when one person sees another person engaging in a behavior and tries to distinguish whether or not they are transgender. We assume that the attacker is not looking to extensively review the person’s history, but is simply trying to identify the person through a single behaviour. In this type of attack, only some of the behaviors being expressed are known, and the attacker tries to find the person who performed the corresponding actions. This is the most difficult attack, but also the most likely attack, as only the behavior expression is required.

A general batch behaviour-only attack is carried out as follows:

The simulator randomly selects a cisgender woman $m_0$ and a transgender woman $m_1$ as the subject.
The simulator uniformly generates a bit $c \gets \{0, 1\}$ at random.
The simulator lets $m_c$ pick some actions to behave and sends the behavior result $b$ to the adversary.
The adversary receives the behaviour $b$ of $m_c$ , and attempts to “guess” whose behaviour it received, and outputs a bit $c'$ .

A transgender has indistinguishable passness under a behaviour-only attack if after running the above experiment the adversary can’t guess correctly $c'=c$ with probability non-negligibly better than $\frac 1 2$ .

3.2 Chosen-Behaviour Attack (CBA) ¶

In practice, however, a hateful transphobia may observe, and even, interact with a person over a long period of time to infer their assigned gender. We therefore propose a stronger attack, the chosen-behaviour attack. In this type of attack, the attacker chooses random actions and obtains the corresponding behaviours and tries to find the transgenders. This attack is easier as a lot of information is already available.

A general batch chosen-behaviour attack is carried out as follows:

The oracle randomly selects a cisgender woman $m_0$ and a transgender woman $m_1$ as the subject.
The oracle uniformly generates a bit $c \gets \{0, 1\}$ at random.
The adversary may choose aribitary $n$ actions.
The adversary then sends these $n$ actions to the behaviour oracle.
The behaviour oracle will then let $m_c$ to behave these actions send the result back to the adversary.
The adversary receives $n$ behaviours back from the oracle, in such a way that the attacker knows which behaviour corresponds to $m_c$ .

Based on these behaviours, the adversary will have lots of information to try to guess the keyword $c$ that the oracle uses to select trans or cis women. After that,

The oracle lets $m_c$ pick some action to do and sends the behavior result $b$ to the adversary.
The adversary receives the behaviour $b$ of $m_c$ , and attempts to “guess” whose behavior it received, and outputs a bit $c'$ .

A transgender has indistinguishable passness under a chosen-behaviour attack if after running the above experiment the adversary can’t guess correctly $c'=c$ with probability non-negligibly better than $\frac 1 2$ .

4. Conclusion ¶

We present Chosen-Behaviour Attack, a cryptographically-inspired approach to trans identification. Our approach sets a new state-of-the-art in imagination, outperforming existing strategies for identifying trans. CBA builds on recent advances in cryptographically-secure passes and shows how their combination can be a powerful approach for identifying trans from behavior. Our future work includes learning and expanding training data in both simulation and the real world.

5. Acknowledgements ¶

Thanks to my Schrodinger’s cat which steadily got nothing when I observe and ChatGPT for their contributions to this article, and thanks to you for the boring time before the final exam. Without you, I would not be able to write such boring things.