Anti-Flickering Deny List

• Anti-Flickering Deny List is a mechanism that identifies and excludes resources exhibiting transient flicker, ensuring persistent revocation in digital systems.
• It employs temporal analytics, statistical thresholding, and auditable registers to maintain consistent access control and data quality.
• Applications span from astrophysical imaging and event-based cameras to defense against adversarial video attacks, enhancing system security and performance.

An Anti-Flickering Deny List is a robust access-control object and data-filtering concept designed to systematically identify, record, and exclude resources, regions, or pixel locations that exhibit undesirable flickering or transient behavior in decentralized systems, sensor arrays, or digital content. Such deny lists are essential for enforcing persistent revocation of access and maintaining data quality in the presence of transient noise, adversarial attacks, or nonstationary artifacts. The construct is notable for its application in fields such as distributed systems (where it underpins access-control primitives), high-throughput astrophysical observation (where it enables reliable imaging), and advanced video processing (where it is used to remove or mark suspect frame regions). Central methodologies depend on resilient object composition (auditability), temporal analytics, and statistical thresholding to guarantee that once a resource is flagged or revoked, it remains persistently denied even under concurrency or system delay.

1. Conceptual Definition and Functional Role

An anti-flickering Deny List is defined as an access–control and data-correction mechanism that enables managers or system components to revoke access or ignore resources in a manner immune to transient “flickering”—that is, unauthorized status persists across interleaving operations and system delay. In distributed systems, it is formalized as an object admitting three operations: append (revoke), prove (query access), and read (audit log). In detector systems, it is a list of identified pixels/events flagged according to statistical or temporal thresholds, ensuring that their data are no longer considered in subsequent analysis or access.

The persistent guarantee (often labeled as immediate Deny List) stipulates that once revoke is issued, any subsequent prove always fails, regardless of transient overlaps. This property intrinsically mitigates risks of temporary resurgent access, which can occur with less robust constructs and is vital for long-term consistency in access control, security, and data integrity.

2. Algorithmic Construction from Auditable Registers

The anti-flickering Deny List is implemented from auditable registers, a synchronization primitive with embedded operation tracking. For a resource $x$ in an %%%%1%%%%-process system, a vector of auditable registers $AR_x[i]$ is maintained—each indexed per process. Initially, all entries are true (access allowed). The operations are implemented as follows:

• append($x$): The revoking manager writes false to its register $AR_x[i]$ and marks a local flag. This operation is always successful.
• prove($x$): To check access, the process inspects its local flag, then reads all other $AR_x[j]$; access is valid only if all remain true.
• read(): The system audits the composite state by repeatedly collecting register values until two consecutive iterations match, yielding a globally consistent snapshot.

These operations rely on the underlying properties of auditable registers—namely, linearizability and wait-freedom—with step complexity $O(n)$ (or $O(n+m)$ for $m$-readers, $n$-writers). The design is efficient and matches lower bounds on synchronization power, specifically consensus number $n$ for $n$-process registers, as formally proven.

Table: Anti-Flickering Deny List Operations

Operation	Description	Step Complexity
append(x)	Revoke access for resource x by writing false	$O(n)$
prove(x)	Check access by reading n-1 registers	$O(n)$
read()	Audit by collecting registers until two successive matches	$O(n)$

3. Principles in Sensor and Event Data Filtering

In high-throughput sensors (e.g., SWIFT BAT event data), an anti-flickering deny list denotes the set of pixels flagged due to intermittent or noisy behavior over varied time scales. Detection algorithms segment the temporal data, isolate intervals with uniform coverage, and construct pulse height analyzer (PHA) histograms to differentiate true signal (Poisson peak) from noise (Gaussian peaks) by thresholding count increases ($x\cdot f$, $x\in[1.01,1.6]$). Flickering pixels are distinguished by analyzing counts in time bins at fast (0.1s), medium (1s), and slow (10s) scales—those exceeding $1\%$ outlier activity ($$\Sigma_{i\in outliers}c_i > 0.01 \cdot \Sigma_{i=1}^N c_i$$) are flagged and replaced with a dummy value $D$, thus entering the deny list.

This differs sharply from prior methods (e.g., BATHOTPIX), which relied only on instantaneous neighbor comparisons and did not consider cumulative behavior over long intervals. The result is cleaner, more reliable data—critical for astrophysical signal detection.

4. Temporal Analysis and Filtering in Event Cameras

For event-based cameras, anti-flickering deny lists manifest as sets of events or pixels removed by linear comb filtering. The seminal algorithm “notches out” periodic flicker with nulls in the transfer function:

$H(s) = 1 - e^{-sT}$

$y(t) = x(t) - x(t-T)$

where $T$ matches flicker period (e.g., $T=1/100$s). An augmented filter introduces feedback for minimal distortion:

$H(s) = \frac{1-e^{-sT}}{1-\rho e^{-sT}}$

Filtered event streams exhibit $>$4.6$\times$ improvement in signal-to-noise ratio, with periodic flicker sources (e.g., fluorescent lighting) efficiently suppressed. Events matching deny list signatures—periodic with respect to $T$—are omitted. This enables robust robotics operation under challenging lighting and sets a basis for subsequent computer vision tasks.

5. Flicker Detection and Deny Lists in Display and Lighting

The elaTCSF model for flicker detection extends temporal contrast sensitivity (TCSF) to include luminance, eccentricity, and stimulus area, yielding a spatially resolved, psychophysically informed deny list. The model’s equations:

$$S_{\text{elaTCSF}}(\omega, L, e) = S_{\mathcal{L}}(L) S_{\mathrm{ecc}}(e) S'_\omega(\omega, L, e)$$

and area integration

$$E(\omega, L, \hat{e}, R) = c^{\beta} \int_{0}^{2\pi}\int_0^R \left[S_{\text{elTCSF}}(\omega, L, d(\hat{e}, r, \theta))\right]^\beta r\, dr\,d\theta$$

allow prediction of flicker visibility thresholds for VR displays, lighting systems, and variable refresh rate signals. Designers utilize deny lists derived from model output to proactively exclude operating points correlated with perceptible flicker, thus adhering to human sensitivity constraints and preempting user discomfort or visual artifacts.

6. Defensive Use in Adversarial Video and Data Embedding

For video recognition and compression models, adversarial flickering attacks (e.g., NetFlick, Over-the-Air Flickering), introduce temporal perturbations (frame-wise RGB offsets) which degrade spatio-temporal correlation and classification performance. Defensive mechanisms may create deny lists by:

• Monitoring global brightness or color changes using first/second temporal derivatives and thickness/roughness regularization.
• Applying pre-processing consistency checks and anomaly detection to filter abnormal frame-to-frame variations.
• Training classifiers and codecs with adversarial data to enforce robustness against flicker.

Similarly, in imperceptible screen-camera communication (e.g., Revelio), data embedding employs flicker fusion in OKLAB space—temporal modulation alternates ($+\lambda d, +\alpha d, +\beta d$ followed by $-\lambda d, -\alpha d, -\beta d$)—crafted so that the flicker is not visible but remains decodable. Such embedding may serve as a forensic marker, forming a deny list of content with unauthorized distribution tags after subsequent analysis.

7. Access Control and Security Implications

Anti-flickering deny lists, as implemented via auditable registers, undergird access control systems where auditability and synchronization power (consensus number) are critical. Auditability records who (and when) accesses resources, supporting strong guarantees in distributed environments—even extending to load-linked/store-conditional objects (LL/SC). Once a revoke is appended, the system state ensures all future proves reflect denial, thereby preventing transient reauthorization. Audit logs furnish post-hoc evidence for compliance, breach detection, or debugging.

The rigorous composition of auditability primitives with deny lists offers security benefits, guarantees linearizability, and ensures that deny actions are globally consistent, abort-free, and wait-free. These properties are fundamental to managing data and access under conditions of high concurrency and uncertainty—the very scenario in which transient flicker, adversarial manipulation, or system race conditions could otherwise compromise the integrity or confidentiality of data.

Summary

The Anti-Flickering Deny List is a resilient, multi-domain construct unifying access control and data correction under the principles of persistent revocation, temporal analysis, and robust auditability. Its effectiveness relies on precise algorithmic construction—statistical thresholding, comb filtering, and register composition—and advanced modeling of perceptual and adversarial flicker. Applications span astrophysical data cleaning, robotics, distributed systems security, advanced video processing, and display engineering, where persistent, auditably enforced denial of flickering regions or resources protects both data quality and system policy.