# Landscape More Secure Than Portrait? Zooming Into the Directionality of Digital Images With Security Implications

arXiv: https://arxiv.org/abs/2406.15206 · HTML: https://mlsanity.com/paper/2406.15206/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 106 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 22 of 22)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023)
- [Detecting Deepfakes with Self-Blended Images](https://mlsanity.com/paper/2204.08376/) (2022)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [Raising the Bar of AI-generated Image Detection with CLIP](https://mlsanity.com/paper/2312.00195/) (2023)
- [Are GAN generated images easy to detect? A critical analysis of the state-of-the-art](https://mlsanity.com/paper/2104.02617/) (2021)
- [The DeepFake Detection Challenge (DFDC) Dataset](https://mlsanity.com/paper/2006.07397/) (2020)
- [Intriguing properties of synthetic images: from generative adversarial networks to diffusion models](https://mlsanity.com/paper/2304.06408/) (2023)
- [Detecting GAN-generated Images by Orthogonal Training of Multiple CNNs](https://mlsanity.com/paper/2203.02246/) (2022)
- [Towards Universal GAN Image Detection](https://mlsanity.com/paper/2112.12606/) (2021)
- [The Forchheim Image Database for Camera Identification in the Wild](https://mlsanity.com/paper/2011.02241/) (2020)
- [RemNet: Remnant Convolutional Neural Network for Camera Model Identification](https://mlsanity.com/paper/1902.00694/) (2019)
- [Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition](https://mlsanity.com/paper/2309.12428/) (2023)
- [An In-Depth Study on Open-Set Camera Model Identification](https://mlsanity.com/paper/1904.08497/) (2019)
- [How to augment a small learning set for improving the performances of a CNN-based steganalyzer?](https://mlsanity.com/paper/1801.04076/) (2018)
- [Web Image Formats: Assessment of Their Real-World-Usage and Performance across Popular Web Browsers](https://mlsanity.com/paper/2310.00788/) (2023)

## Built on by (most cited first, 2 of 2)

- [Adaptive Dual-Path Framework for Covert Semantic Communication](https://mlsanity.com/paper/2605.03423/) (2026)
- [BIAS-ID: A Framework for Analyzing Transformation Biases in AI-Generated Image Detectors](https://mlsanity.com/paper/2605.31153/) (2026)

## Abstract (arXiv)

The orientation in which a source image is captured can affect the resulting security in downstream applications. One reason for this is that many state-of-the-art methods in media security assume that image statistics are similar in the horizontal and vertical directions, allowing them to reduce the number of features (or trainable weights) by merging coefficients. We show that this artificial symmetrization tends to suppress important properties of natural images and common processing operations, causing a loss of performance. We also observe the opposite problem, where unaddressed directionality causes learning-based methods to overfit to a single orientation. These are vulnerable to manipulation if an adversary chooses inputs with the less common orientation. This paper takes a comprehensive approach, identifies and systematizes causes of directionality at several stages of a typical acquisition pipeline, measures their effect, and demonstrates for three selected security applications (steganalysis, forensic source identification, and the detection of synthetic images) how the performance of state-of-the-art methods can be improved by properly accounting for directionality.