SKU: 5722884293

TINY™ Collection — On Par Wallpaper

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Description

TINY™ Collection — On Par WallpaperThis piece is part of the TINYCollection, a signature series by KPalm. Custom wallpaper available upon request. Grasscloth: Pattern repeat: 34 W x 34 H Paper width: 36" Printed width: 34" wide Lead time: Approximately 7 10 days We highly recommend all wallpaper measurements and installation be carried out by a professional. Over time, wallpaper can shrink, causing seams to become visible. Since walls must be primed prior to installing wallpaper, if

This piece is part of the TINY™Collection, a signature series by KPalm.

Custom wallpaper available upon request.

Grasscloth:

Pattern repeat: 34” W x 34” H
Paper width: 36"
Printed width: 34" wide
Lead time: Approximately 7-10 days

We highly recommend all wallpaper measurements and installation be carried out by a professional. 

 Over time, wallpaper can shrink, causing seams to become visible. Since walls must be primed prior to installing wallpaper, if the wallpaper you have selected has a darker, more saturated background we recommend selecting a primer that matches the color of your wallpaper. This will make the seams less visible should they appear. 

 Please note: the ideal temperature for wallpapering and drying is about 65 degrees Fahrenheit.  If possible, adjust room to this temperature and leave wallpaper rolls in the room for 48 hours prior to installation, allowing the wallpaper to acclimate to the room and temperature. Then maintain this temperature for a few days after installation.

For coordinating fabric options, please contact us via email at [email protected].

Disclaimer: KPalm Fine Art is not responsible for measurement errors made by the customer or installer. We strongly recommend ordering at least 20% extra wallpaper to allow for pattern matching, trimming, and installation adjustments.

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Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
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SKU: 5722884293

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TheTechGuyz
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★★★★★ 5
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A very good book, I absolutely love how it gives security professionals much needed AI security insights.
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Reviewed in the United States on January 16, 2026
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★★★★★ 5
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"Adversarial AI Attacks, Mitigations, and Defense Strategies" by John Sotiropoulos is a must-have resource for cybersecurity professionals navigating the complexities of AI security. This book is an incredibly in-depth guide that tackles the intricate details of defending AI systems from adversarial attacks. It’s highly technical, making it an excellent choice for those with a solid background in cybersecurity, machine learning, and system administration. Sotiropoulos doesn’t shy away from the details, providing comprehensive code examples, system admin settings, and scripts that are invaluable for practical implementation. One of the standout aspects of this book is its coverage of both predictive and generative AI. This dual focus ensures that readers are well-equipped to handle security challenges across different AI applications. Whether you're dealing with machine learning models in a predictive context or exploring the relatively newer field of generative AI, this book has you covered. If you’re looking for a technical, hands-on approach to securing AI systems, this book is an essential addition to your library.
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Reviewed in the United States on August 12, 2024
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★★★★★ 4
Good and thorough!
Format: Paperback
I was amazed to see a thick book arriving in the package and spent quite some time reading this. The book is so hands-on. I build agentic systems at work and going through these concepts felt good. My only complaint is that the code snippets are not up to date for which I had to edit my code several times.
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Reviewed in the United States on May 9, 2026
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Catalina J.
Los Angeles, US
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Excelent product
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Reviewed in the United States on November 4, 2025
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Brian
Cuba, US
★★★★★ 5
solid read with walk through
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Reviewed in the United States on October 18, 2024

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