Search Results for author:
Found 11 papers, 7 papers with code
AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images
Recent advances in the field of generative artificial intelligence (AI) have blurred the lines between authentic and machine-generated content, making it almost impossible for humans to distinguish between such media.
The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision
We empirically examine our findings in a comprehensive evaluation with multiple image classification models and show that our attack achieves the same sparsity effect as prior sponge-example methods, but at a fraction of computation effort.
On the Detection of Image-Scaling Attacks in Machine Learning
Unfortunately, this preprocessing step is vulnerable to so-called image-scaling attacks where an attacker makes unnoticeable changes to an image so that it becomes a new image after scaling.
No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning
In this paper, we show that this automation can be manipulated using adversarial learning.
I still know it's you! On Challenges in Anonymizing Source Code
We prove that the task of generating a $k$-anonymous program -- a program that cannot be attributed to one of $k$ authors -- is not computable in the general case.
Misleading Deep-Fake Detection with GAN Fingerprints
In particular, we show that an adversary can remove indicative artifacts, the GAN fingerprint, directly from the frequency spectrum of a generated image.
Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification
Consequently, adversaries will also target the learning system and use evasion attacks to bypass the detection of malware.
Dos and Don'ts of Machine Learning in Computer Security
With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas.
Backdooring and Poisoning Neural Networks with Image-Scaling Attacks
By combining poisoning and image-scaling attacks, we can conceal the trigger of backdoors as well as hide the overlays of clean-label poisoning.
Misleading Authorship Attribution of Source Code using Adversarial Learning
In this paper, we present a novel attack against authorship attribution of source code.
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking
This problem has motivated the research field of adversarial machine learning that is concerned with attacking and defending learning methods.
