To provide the forensic community with unique and relevant digital forensic software solutions and training while adhering to our core values of honor, integrity, loyalty, positive attitude and dedication.

RECON ITR combines the power of RECON IMAGER PRO and RECON TRIAGE into one product that covers all Intel and Apple Silicon Macs. The only tool that’s built natively on the macOS platform, RECON ITR gives you the answers you need within minutes or seconds with an automated analysis feature.

RECON ITR combines both the functions of bootable and live imaging from RECON TRIAGE and RECON IMAGER PRO into an integrated solution that automatically identifies, displays, and can image FileVault, APFS, and Core Storage volumes and disks at a cost well below any other tool.

Image recon is a tool used to find the social media accounts associated with the uploading image. Topics hacking python3 cybersecurity reconnaissance osint-tool

RECON IMAGER is a forensic imaging software, developed by SUMURI for macOS, and is based on OS X. RECON IMAGER can image all Intel® based Apple computers, including the newest generation of MacBook Pro with Touch Bar.

To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction. We call this network, ReconNet. The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image.

In this paper, we propose ReconX, a novel 3D scene reconstruction paradigm that reframes the ambiguous reconstruction challenge as a temporal generation task. The key insight is to unleash the strong generative prior of large pre-trained video diffusion models for …

In today's video, join Chris from the SUMURI software team as he shows you how to boot and image an M1 Mac with the latest version of RECON ITR! Make sure you're ready for these new Macs by...

2021年3月4日 · 生成对抗网络运行的基本原理是：一个生成器试图“愚弄”一个鉴别器，一个确定的鉴别器试图区分出生成器生成的图像。 换句话说，两个网络试图分别使损失函数最小化和最大化。 区域掩模是我们所屏蔽的图像的一部分，这样我们就可以将生成的修复问题提供给模型。 通过屏蔽，我们将该图像区域的像素值设置为0。 有三种方法： 中心区域：对图像数据进行遮挡，最简单的方法是将中心的正方形斑块设为零。 虽然网络学习修复，但我们面临着泛化的问题。 该网络 …

2021年3月4日 · 图像修复是人工智能研究的一个活跃领域，人工智能已经能够得出比大多数艺术家更好的修复结果。 在本文中，我们将讨论使用 神经网络，特别是上下文编码器的图像修复。 本文解释并实现了在CVPR 2016中提出的关于上下文编码器的研究工作。 为了开始使用上下文编码器，我们必须了解什么是“ 自编码器 ”。 自编码器在结构上由编码器、解码器以及一个bottleneck组成。 一般的自编码器的目的是通过忽略图像中的噪声来减小图像的尺寸。 然而，自编码器不是 …