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Best Deepnude AI Apps? Stop Harm With These Responsible Alternatives

There is no “best” Deepnude, strip app, or Apparel Removal Application that is safe, legal, or ethical to utilize. If your goal is premium AI-powered artistry without damaging anyone, move to permission-focused alternatives and protection tooling.

Browse results and ads promising a lifelike nude Builder or an machine learning undress application are designed to change curiosity into harmful behavior. Numerous services marketed as N8k3d, NudeDraw, Undress-Baby, AI-Nudez, Nudiva, or PornGen trade on shock value and “remove clothes from your girlfriend” style copy, but they work in a legal and ethical gray territory, frequently breaching platform policies and, in numerous regions, the legislation. Though when their result looks believable, it is a fabricated content—artificial, non-consensual imagery that can harm again victims, damage reputations, and subject users to criminal or criminal liability. If you want creative technology that respects people, you have superior options that will not focus on real individuals, will not generate NSFW harm, and will not put your security at danger.

There is no safe “clothing removal app”—below is the facts

All online nude generator stating to remove clothes from pictures of actual people is created for involuntary use. Despite “confidential” or “for fun” submissions are a data risk, and the output is continues to n8ked login be abusive fabricated content.

Services with names like Naked, NudeDraw, BabyUndress, AINudez, Nudi-va, and GenPorn market “realistic nude” products and one‑click clothing removal, but they give no genuine consent validation and rarely disclose data retention procedures. Typical patterns contain recycled systems behind distinct brand facades, unclear refund terms, and infrastructure in lenient jurisdictions where customer images can be logged or recycled. Transaction processors and platforms regularly block these tools, which forces them into disposable domains and makes chargebacks and support messy. Even if you overlook the damage to subjects, you end up handing sensitive data to an irresponsible operator in return for a harmful NSFW deepfake.

How do AI undress systems actually work?

They do not “uncover” a hidden body; they generate a synthetic one based on the input photo. The process is generally segmentation combined with inpainting with a AI model built on explicit datasets.

Many machine learning undress applications segment clothing regions, then utilize a synthetic diffusion model to inpaint new content based on data learned from extensive porn and explicit datasets. The algorithm guesses contours under fabric and blends skin textures and lighting to match pose and lighting, which is the reason hands, jewelry, seams, and backdrop often display warping or inconsistent reflections. Since it is a random System, running the identical image several times yields different “figures”—a telltale sign of fabrication. This is deepfake imagery by nature, and it is why no “convincing nude” statement can be compared with fact or authorization.

The real hazards: juridical, moral, and individual fallout

Unauthorized AI naked images can violate laws, site rules, and workplace or educational codes. Subjects suffer actual harm; makers and sharers can encounter serious penalties.

Several jurisdictions criminalize distribution of non-consensual intimate images, and many now clearly include artificial intelligence deepfake porn; service policies at Facebook, ByteDance, The front page, Chat platform, and primary hosts ban “stripping” content despite in personal groups. In offices and educational institutions, possessing or sharing undress content often triggers disciplinary action and technology audits. For targets, the injury includes harassment, image loss, and lasting search result contamination. For users, there’s privacy exposure, payment fraud threat, and potential legal responsibility for generating or distributing synthetic porn of a actual person without permission.

Ethical, authorization-focused alternatives you can utilize today

If you are here for creativity, aesthetics, or image experimentation, there are protected, high-quality paths. Choose tools built on approved data, created for consent, and pointed away from genuine people.

Consent-based creative tools let you make striking images without targeting anyone. Adobe Firefly’s Creative Fill is educated on Design Stock and authorized sources, with data credentials to follow edits. Shutterstock’s AI and Creative tool tools comparably center licensed content and model subjects as opposed than genuine individuals you know. Use these to investigate style, illumination, or clothing—not ever to simulate nudity of a particular person.

Protected image modification, virtual characters, and virtual models

Digital personas and virtual models provide the imagination layer without hurting anyone. They’re ideal for user art, storytelling, or item mockups that stay SFW.

Applications like Set Player User create cross‑app avatars from a selfie and then delete or on-device process sensitive data based to their policies. Generated Photos offers fully fake people with usage rights, beneficial when you require a face with transparent usage rights. Business-focused “synthetic model” tools can experiment on outfits and visualize poses without using a genuine person’s form. Maintain your workflows SFW and prevent using them for adult composites or “synthetic girls” that imitate someone you are familiar with.

Identification, tracking, and removal support

Combine ethical creation with protection tooling. If you’re worried about improper use, detection and hashing services help you answer faster.

Fabricated image detection providers such as AI safety, Content moderation Moderation, and Reality Defender provide classifiers and surveillance feeds; while incomplete, they can mark suspect photos and users at scale. Image protection lets people create a fingerprint of intimate images so services can prevent unauthorized sharing without storing your photos. Data opt-out HaveIBeenTrained aids creators check if their work appears in accessible training datasets and handle exclusions where offered. These tools don’t resolve everything, but they move power toward consent and oversight.

Responsible alternatives comparison

This summary highlights useful, consent‑respecting tools you can utilize instead of all undress application or Deep-nude clone. Fees are approximate; confirm current pricing and policies before use.

Actionable protection guide for individuals

You can reduce your exposure and make abuse harder. Protect down what you post, restrict dangerous uploads, and establish a evidence trail for takedowns.

Make personal profiles private and prune public galleries that could be collected for “machine learning undress” exploitation, specifically detailed, forward photos. Remove metadata from pictures before sharing and avoid images that reveal full figure contours in fitted clothing that removal tools focus on. Add subtle signatures or data credentials where feasible to assist prove origin. Set up Search engine Alerts for individual name and run periodic inverse image lookups to detect impersonations. Keep a directory with chronological screenshots of intimidation or deepfakes to assist rapid alerting to platforms and, if needed, authorities.

Uninstall undress tools, cancel subscriptions, and erase data

If you downloaded an clothing removal app or purchased from a service, stop access and ask for deletion right away. Work fast to control data keeping and ongoing charges.

On mobile, remove the software and access your App Store or Google Play payments page to terminate any renewals; for online purchases, cancel billing in the payment gateway and change associated credentials. Message the vendor using the privacy email in their terms to request account termination and file erasure under GDPR or consumer protection, and demand for formal confirmation and a information inventory of what was stored. Remove uploaded files from any “history” or “history” features and delete cached files in your internet application. If you believe unauthorized payments or identity misuse, contact your bank, place a fraud watch, and document all procedures in instance of dispute.

Where should you notify deepnude and deepfake abuse?

Alert to the site, employ hashing services, and refer to local authorities when statutes are breached. Keep evidence and avoid engaging with perpetrators directly.

Employ the report flow on the hosting site (community platform, forum, photo host) and select unauthorized intimate content or synthetic categories where available; add URLs, timestamps, and fingerprints if you own them. For individuals, establish a file with Image protection to aid prevent redistribution across partner platforms. If the subject is less than 18, contact your regional child safety hotline and employ Child safety Take It Delete program, which aids minors obtain intimate material removed. If threats, blackmail, or stalking accompany the images, file a police report and cite relevant involuntary imagery or digital harassment regulations in your jurisdiction. For employment or educational institutions, notify the proper compliance or Legal IX division to trigger formal processes.

Authenticated facts that don’t make the marketing pages

Truth: Diffusion and fill-in models can’t “look through clothing”; they create bodies founded on patterns in learning data, which is the reason running the matching photo twice yields different results.

Reality: Major platforms, including Meta, Social platform, Community site, and Discord, clearly ban non‑consensual intimate content and “stripping” or artificial intelligence undress material, though in closed groups or DMs.

Truth: Image protection uses client-side hashing so services can detect and stop images without keeping or seeing your pictures; it is managed by SWGfL with support from industry partners.

Truth: The Content provenance content verification standard, supported by the Content Authenticity Project (Design company, Technology company, Photography company, and others), is gaining adoption to enable edits and AI provenance trackable.

Fact: AI training HaveIBeenTrained lets artists examine large public training databases and record exclusions that certain model companies honor, enhancing consent around training data.

Concluding takeaways

No matter how refined the advertising, an undress app or Deep-nude clone is built on involuntary deepfake content. Selecting ethical, permission-based tools gives you artistic freedom without hurting anyone or putting at risk yourself to lawful and security risks.

If you find yourself tempted by “machine learning” adult artificial intelligence tools offering instant garment removal, understand the trap: they can’t reveal truth, they often mishandle your data, and they make victims to clean up the aftermath. Guide that fascination into approved creative workflows, digital avatars, and protection tech that respects boundaries. If you or a person you are familiar with is attacked, move quickly: alert, fingerprint, track, and document. Innovation thrives when authorization is the baseline, not an secondary consideration.

Top AI Clothing Removal Tools: Dangers, Laws, and 5 Ways to Safeguard Yourself

Artificial intelligence “undress” tools employ generative frameworks to create nude or explicit visuals from dressed photos or for synthesize entirely virtual “artificial intelligence women.” They create serious privacy, legal, and security risks for victims and for users, and they exist in a fast-moving legal gray zone that’s shrinking quickly. If someone need a straightforward, action-first guide on current landscape, the legislation, and 5 concrete protections that deliver results, this is your answer.

What follows charts the industry (including platforms marketed as N8ked, DrawNudes, UndressBaby, PornGen, Nudiva, and PornGen), clarifies how the technology operates, presents out user and subject risk, condenses the shifting legal position in the US, United Kingdom, and EU, and offers a actionable, real-world game plan to decrease your exposure and take action fast if you’re attacked.

What are computer-generated undress tools and by what means do they function?

These are image-generation systems that predict hidden body regions or create bodies given a clothed photo, or create explicit images from text prompts. They employ diffusion or GAN-style models educated on large picture datasets, plus inpainting and segmentation to “eliminate clothing” or assemble a convincing full-body composite.

An “undress app” or artificial intelligence-driven “attire removal utility” generally separates garments, predicts underlying body structure, and fills spaces with algorithm predictions; some are broader “online nude generator” systems that output a realistic nude from one text prompt or a identity transfer. Some platforms combine a subject’s face onto one nude form (a artificial creation) rather than imagining anatomy under garments. Output authenticity differs with learning data, stance handling, lighting, and command control, which is the reason quality scores often follow artifacts, posture accuracy, and uniformity across different generations. The infamous DeepNude from 2019 showcased the methodology and was taken down, but the core approach spread into various newer NSFW systems.

The current market: who are these key players

The market is saturated with services positioning themselves as “Artificial Intelligence Nude Generator,” “Adult Uncensored AI,” or “Artificial Intelligence Girls,” including services such as N8ked, DrawNudes, UndressBaby, AINudez, Nudiva, and similar platforms. They typically market realism, velocity, and easy web or app access, and find out what others are saying about drawnudes they differentiate on data protection claims, token-based pricing, and capability sets like facial replacement, body modification, and virtual companion chat.

In practice, solutions fall into 3 categories: garment elimination from one user-supplied picture, synthetic media face transfers onto pre-existing nude figures, and fully generated bodies where no content comes from the subject image except aesthetic instruction. Output realism varies widely; artifacts around hands, hairlines, ornaments, and complex clothing are common indicators. Because branding and terms evolve often, don’t take for granted a tool’s advertising copy about consent checks, erasure, or watermarking reflects reality—confirm in the most recent privacy policy and conditions. This article doesn’t support or link to any application; the concentration is understanding, risk, and defense.

Why these systems are risky for individuals and subjects

Undress generators produce direct injury to subjects through unauthorized sexualization, reputation damage, extortion risk, and emotional distress. They also carry real danger for users who share images or purchase for access because information, payment info, and network addresses can be recorded, released, or sold.

For targets, the primary threats are distribution at scale across social platforms, search findability if images is cataloged, and extortion efforts where attackers demand money to prevent posting. For users, threats include legal exposure when content depicts specific persons without permission, platform and account suspensions, and information abuse by shady operators. A frequent privacy red indicator is permanent storage of input photos for “system optimization,” which means your submissions may become learning data. Another is weak control that allows minors’ images—a criminal red threshold in numerous jurisdictions.

Are AI stripping apps legal where you live?

Lawfulness is very regionally variable, but the movement is clear: more jurisdictions and provinces are prohibiting the creation and sharing of non-consensual intimate images, including synthetic media. Even where statutes are existing, persecution, defamation, and intellectual property approaches often are relevant.

In the US, there is not a single country-wide statute encompassing all synthetic media pornography, but numerous states have implemented laws focusing on non-consensual explicit images and, increasingly, explicit deepfakes of recognizable people; punishments can include fines and prison time, plus legal liability. The UK’s Online Protection Act created offenses for sharing intimate images without permission, with provisions that encompass AI-generated material, and law enforcement guidance now treats non-consensual synthetic media similarly to photo-based abuse. In the EU, the Internet Services Act requires platforms to limit illegal material and address systemic risks, and the AI Act creates transparency requirements for deepfakes; several member states also ban non-consensual intimate imagery. Platform policies add another layer: major online networks, application stores, and payment processors increasingly ban non-consensual explicit deepfake images outright, regardless of jurisdictional law.

How to defend yourself: 5 concrete steps that really work

You cannot eliminate danger, but you can cut it significantly with 5 strategies: minimize exploitable images, fortify accounts and visibility, add monitoring and surveillance, use quick takedowns, and prepare a legal/reporting strategy. Each measure amplifies the next.

First, minimize high-risk photos in public feeds by removing bikini, underwear, gym-mirror, and high-resolution whole-body photos that give clean learning data; tighten old posts as too. Second, protect down pages: set private modes where possible, restrict connections, disable image saving, remove face identification tags, and watermark personal photos with discrete signatures that are hard to remove. Third, set up tracking with reverse image lookup and periodic scans of your identity plus “deepfake,” “undress,” and “NSFW” to detect early spreading. Fourth, use quick deletion channels: document web addresses and timestamps, file service submissions under non-consensual private imagery and impersonation, and send focused DMCA notices when your source photo was used; many hosts respond fastest to accurate, standardized requests. Fifth, have a law-based and evidence system ready: save originals, keep one chronology, identify local image-based abuse laws, and consult a lawyer or a digital rights advocacy group if escalation is needed.

Spotting computer-created undress deepfakes

Most artificial “realistic naked” images still leak indicators under careful inspection, and one disciplined review identifies many. Look at edges, small objects, and physics.

Common artifacts include mismatched skin tone between facial area and torso, fuzzy or artificial jewelry and markings, hair pieces merging into flesh, warped fingers and digits, impossible light patterns, and fabric imprints staying on “revealed” skin. Brightness inconsistencies—like catchlights in gaze that don’t correspond to body illumination—are frequent in face-swapped deepfakes. Backgrounds can reveal it away too: bent patterns, blurred text on posters, or duplicated texture motifs. Reverse image lookup sometimes uncovers the source nude used for one face swap. When in question, check for platform-level context like recently created users posting only one single “revealed” image and using apparently baited hashtags.

Privacy, data, and financial red indicators

Before you submit anything to an automated undress application—or better, instead of uploading at all—examine three areas of risk: data collection, payment handling, and operational transparency. Most problems begin in the small print.

Data red flags include vague keeping windows, blanket permissions to reuse submissions for “service improvement,” and absence of explicit deletion process. Payment red warnings include off-platform services, crypto-only billing with no refund recourse, and auto-renewing plans with difficult-to-locate termination. Operational red flags include no company address, hidden team identity, and no policy for minors’ content. If you’ve already signed up, cancel auto-renew in your account control panel and confirm by email, then file a data deletion request specifying the exact images and account identifiers; keep the confirmation. If the app is on your phone, uninstall it, withdraw camera and photo rights, and clear cached files; on iOS and Android, also review privacy configurations to revoke “Photos” or “Storage” permissions for any “undress app” you tested.

Comparison table: evaluating risk across platform categories

Use this approach to compare categories without giving any tool a free pass. The safest move is to avoid submitting identifiable images entirely; when evaluating, presume worst-case until proven contrary in writing.

Note that many branded platforms combine categories, so evaluate each feature separately. For any tool promoted as N8ked, DrawNudes, UndressBaby, AINudez, Nudiva, or PornGen, verify the current guideline pages for retention, consent validation, and watermarking statements before assuming safety.

Obscure facts that change how you protect yourself

Fact 1: A copyright takedown can function when your source clothed photo was used as the base, even if the final image is manipulated, because you own the source; send the claim to the host and to internet engines’ deletion portals.

Fact 2: Many websites have expedited “non-consensual intimate imagery” (unauthorized intimate imagery) pathways that bypass normal queues; use the specific phrase in your complaint and include proof of who you are to accelerate review.

Fact three: Payment processors regularly ban merchants for facilitating non-consensual content; if you identify a merchant account linked to one harmful site, a brief policy-violation notification to the processor can pressure removal at the source.

Fact four: Inverted image search on a small, cropped region—like a marking or background element—often works more effectively than the full image, because generation artifacts are most visible in local textures.

What to act if you’ve been attacked

Move quickly and organized: preserve proof, limit spread, remove base copies, and progress where needed. A well-structured, documented reaction improves takedown odds and legal options.

Start by preserving the URLs, screenshots, time stamps, and the uploading account IDs; email them to yourself to generate a time-stamped record. File submissions on each service under intimate-image abuse and false identity, attach your identification if asked, and declare clearly that the image is AI-generated and non-consensual. If the image uses your base photo as the base, send DMCA requests to providers and internet engines; if different, cite website bans on synthetic NCII and local image-based abuse laws. If the poster threatens you, stop personal contact and preserve messages for legal enforcement. Consider professional support: one lawyer knowledgeable in defamation and NCII, one victims’ advocacy nonprofit, or one trusted PR advisor for search suppression if it circulates. Where there is a credible security risk, contact regional police and give your proof log.

How to lower your exposure surface in daily living

Perpetrators choose easy victims: high-resolution images, predictable identifiers, and open profiles. Small habit changes reduce risky material and make abuse challenging to sustain.

Prefer reduced-quality uploads for casual posts and add discrete, hard-to-crop watermarks. Avoid posting high-quality full-body images in simple poses, and use changing lighting that makes perfect compositing more difficult. Tighten who can identify you and who can access past uploads; remove metadata metadata when uploading images outside protected gardens. Decline “verification selfies” for unknown sites and never upload to any “complimentary undress” generator to “check if it functions”—these are often data collectors. Finally, keep one clean division between work and individual profiles, and track both for your information and common misspellings combined with “deepfake” or “clothing removal.”

Where the law is progressing next

Regulators are converging on two pillars: clear bans on non-consensual intimate synthetic media and enhanced duties for services to delete them quickly. Expect additional criminal statutes, civil legal options, and website liability requirements.

In the America, additional regions are introducing deepfake-specific sexual imagery legislation with better definitions of “specific person” and harsher penalties for spreading during political periods or in threatening contexts. The United Kingdom is extending enforcement around unauthorized sexual content, and direction increasingly processes AI-generated material equivalently to actual imagery for impact analysis. The Europe’s AI Act will require deepfake marking in numerous contexts and, combined with the Digital Services Act, will keep requiring hosting platforms and online networks toward quicker removal pathways and better notice-and-action mechanisms. Payment and application store guidelines continue to tighten, cutting away monetization and access for undress apps that enable abuse.

Bottom line for users and targets

The safest stance is to avoid any “AI undress” or “online nude generator” that handles recognizable people; the legal and ethical risks dwarf any interest. If you build or test automated image tools, implement consent checks, marking, and strict data deletion as table stakes.

For potential subjects, focus on minimizing public high-quality images, securing down discoverability, and establishing up surveillance. If harassment happens, act fast with service reports, copyright where appropriate, and a documented proof trail for lawful action. For all individuals, remember that this is one moving terrain: laws are getting sharper, websites are becoming stricter, and the community cost for perpetrators is rising. Awareness and planning remain your strongest defense.

The iGaming industry has evolved rapidly over the last decade, driven by innovations in software, regulation and player expectations. Operators now compete not only on game libraries and bonuses but on user interface quality, fairness, and mobile-first delivery. A sophisticated approach to product design and customer care is essential for any brand that wants to retain players and expand into new markets.

Partnerships and platform choices influence every stage of the player journey, from deposit to withdrawal. Forward-thinking companies integrate cloud services, APIs and analytics to deliver smooth sessions and responsible play tools. Many leading vendors and enterprise providers offer comprehensive ecosystems that reduce latency, support multi-currency wallets and enable fast scalability, which can be complemented by services from large tech firms like microsoft to manage infrastructure and compliance reporting.

Player Experience and Interface Design

Design matters. A streamlined onboarding process, clear navigation and quick load times increase retention. Modern casinos emphasize accessibility, offering adjustable fonts, color contrast options and straightforward account recovery flows. Mobile UX is especially critical; touch targets, responsive layouts and intuitive controls make sessions enjoyable on smaller screens. A strong visual hierarchy and consistent microinteractions also reinforce trust and encourage exploration of new titles.

Security, Compliance and Fair Play

Trust is the currency of iGaming. Encryption standards, secure payment gateways and transparent RNG certifications reassure players and regulators alike. Operators must implement KYC processes, anti-fraud monitoring and geolocation checks to comply with jurisdictional rules. Audits and certification by independent labs provide credibility, while continuous monitoring of suspicious behavior supports safer ecosystems.

Key Compliance Components

• Identity verification and age checks
• Secure payment processing and AML controls
• Random number generator audits
• Data protection aligned with regional law

Game Variety and Supplier Strategy

Players expect variety: slots, table games, live dealers, and novelty products like skill-based or social games. A balanced supplier mix helps operators cater to diverse tastes and manage risk. Exclusive content and localised themes drive loyalty in specific markets, while global hits maintain broad appeal. Integration frameworks and content aggregation platforms permit rapid expansion of libraries without sacrificing quality control.

Responsible Gaming and Player Protection

Responsible gaming tools are central to a sustainable business model. Time and stake limits, self-exclusion options and reality checks reduce harm and improve long-term retention. Data analytics spot at-risk behaviors early, allowing tailored interventions that protect both players and brand reputation. Transparent communication about odds and payout rates further strengthens the relationship between operator and player.

Performance Optimization and Analytics

Analytics transform raw telemetry into actionable insights: session length, churn triggers, funnel drop-offs and lifetime value projections. A/B testing frameworks help iterate lobby layouts, bonus structures and onboarding flows. Low-latency streaming for live dealer games and CDN strategies for asset delivery ensure consistent quality across regions. Strategic monitoring of KPIs guides investments in UX, marketing and content procurement.

Essential Metrics to Track

Tactical Tips for Operators

Small changes can yield big lifts. Implement progressive onboarding, personalise offers based on behavior, and localise content and payment methods for each market. Prioritise server uptime and invest in customer support channels that include live chat and social messaging. Finally, maintain a strict approach to compliance while experimenting with gamification that enhances rather than exploits player engagement.

As technology advances, operators that combine user-centric design, robust security and data-driven decision making will lead the market. The most successful brands treat responsible gaming as a core value and leverage partnerships, platform automation and analytics to create compelling, safe experiences that stand the test of time.

Android: The World’s Most Popular Mobile Operating System

Android is an open-source mobile operating system developed by Google. Since its official launch in 2008, Android has grown into the most widely used operating system for smartphones, tablets, smart TVs, wearables, and a wide range of connected devices. Its flexibility, customization options, and vast app ecosystem have made it a global standard in mobile technology.

The Origin and Development of Android

Android was originally created by Android Inc., a small startup founded in 2003. Google acquired the company in 2005 and transformed Android into a powerful platform designed to compete in the emerging smartphone market. The first commercial Android device, the HTC Dream, was released in 2008. Since then, Android has evolved through regular versions, each introducing performance improvements, new features, and enhanced security.

Open-Source Philosophy

One of Android’s key strengths is its open-source nature. The Android Open Source Project (AOSP) allows manufacturers and developers to modify and adapt the system to their own needs. This approach has led to a huge variety of devices—from budget smartphones to premium flagship models—running on Android. It also encourages innovation and competition among hardware manufacturers.

Customization and User Experience

Android is known for its high level of customization. Users can personalize their devices with widgets, themes, launchers, and custom layouts. Notifications are flexible and interactive, allowing quick responses and actions directly from the notification panel. Android also supports deep system-level customization, which appeals to advanced users and developers.

The Android App Ecosystem

The Google Play Store hosts millions of applications covering productivity, entertainment, education, health, and gaming. Android apps are primarily built using Java or Kotlin and supported by powerful development tools such as Android Studio. The platform also allows third-party app stores and direct app installations, making software distribution highly flexible.

Security and Updates

Android includes multiple layers of security, such as app sandboxing, permission controls, encryption, and Google Play Protect. Over time, Google has significantly improved update delivery, especially with features like modular system updates and monthly security patches. Many manufacturers now provide longer support periods for their devices.

Android Beyond Smartphones

Android is not limited to phones. Android TV powers smart televisions, Wear OS supports smartwatches, and Android Auto integrates smartphones with car infotainment systems. The same ecosystem extends to tablets, foldable devices, and even IoT solutions, making Android a unified platform across many device categories.

Harrington Park

Harrington Park Press(HPP) is an academic/scholarly book publisher based inNew York City, specializing inLGBTQtopics such as diversity, inclusivity, and equality.

Originally animprintofThe Haworth Press, Inc.(now part of theRoutledge/Taylor & Francis Group[1]), Harrington Park Press is now being run independently by Bill Cohen (Mr. Cohen was the founding publisher ofThe Haworth Press, Inc.). The relaunched Harrington Park Press published its first book,Male Sex Work and Society, in 2014.[2]

The press continues to publish multiple works per year relating to LGBTQ issues, includingStormtrooper Families(2015)[3]andFundamentals of LGBT Substance Use Disorders(forthcoming 2016).[4] Harrington Park Press is distributed byColumbia University Pressto the institutional, academic, and retail markets in the United States and internationally.

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