DOI: 10.5281/zenodo.15423637
title: Spam Images in Messaging - Annotated Set (SIMAS)

description: SIMAS Dataset



This archive includes the SIMAS dataset for fine-tuning models for MMS (Multimedia Messaging Service) image moderation. SIMAS is a balanced collection of publicly available images, manually annotated in accordance with a specialized taxonomy designed for identifying visual spam in MMS messages.


Taxonomy for MMS Visual Spam

The following table presents the definitions of categories used for classifying MMS images.

Table 1: Category definitions

  


Category   
Description 


Alcohol*   
Content related to alcoholic beverages, including advertisements and consumption. 


Drugs*     
Content related to the use, sale, or trafficking of narcotics (e.g., cannabis, cocaine,


Firearms*  
Content involving guns, pistols, knives, or military weapons. 


Gambling*  
Content related to gambling (casinos, poker, roulette, lotteries). 


Sexual     
Content involving nudity, sexual acts, or sexually suggestive material. 


Tobacco*   
Content related to tobacco use and advertisements. 


Violence   
Content showing violent acts, self-harm, or injury. 


Safe       
All other content, including neutral depictions, products, or harmless cultural symbols




Note: Categories marked with an asterisk are regulated in some jurisdictions and may not be universally restricted.

Dataset Collection and Annotation

Data Sources

The SIMAS dataset combines publicly available images from multiple sources, selected to reflect the categories defined in our content taxonomy. Each image was manually reviewed by three independent annotators, with final labels assigned when at least two annotators agreed.

The largest portion of the dataset (30.4%) originates from LAION-400M, a large-scale image-text dataset. To identify relevant content, we first selected a list of ImageNet labels that semantically matched our taxonomy. These labels were generated using GPT-4o in a zero-shot setting, using separate prompts per category. This resulted in 194 candidate labels, of which 88.7% were retained after manual review. The structure of the prompts used in this process is shown in the file gpt4o_imagenet_prompting_scheme.png, which illustrates a shared base prompt template applied across all categories. The fields category_definition, file_examples, and exceptions are specified per category. Definitions align with the taxonomy, while the file_examples column includes sample labels retrieved from the ImageNet label list. The exceptions field contains category-specific filtering instructions; a dash indicates no exceptions were specified.

Another 25.1% of images were sourced from Roboflow, using open datasets such as:



Marijuana and Hemp 200

Drug Detection

Plants Classification

Weapon Detection

Suicide Detection

Violence Detection

Clasificacionimagenes

Waste Recognition



FYP



The NudeNet dataset contributes 11.4% of the dataset. We sampled 1,000 images from the “porn” category to provide visual coverage of explicit sexual content.

Another 11.0% of images were collected from Kaggle, including:



National Flowers

Weapon Dataset for YOLOv5

GUIE Toys

Alcohol Bottle Images

Smoking & Drinking Dataset


An additional 9.9% of images were retrieved from Unsplash, using keyword-based search queries aligned with each category in our taxonomy.

Images from UnsafeBench make up 8.0% of the dataset. Since its original binary labels did not match our taxonomy, all samples were manually reassigned to the most appropriate category.

Finally, 4.2% of images were gathered from various publicly accessible websites. These were primarily used to improve category balance and model generalization, especially in safe classes.

All images collected from the listed sources have been manually reviewed by three independent annotators. Each image is then assigned to a category when at least two annotators reach consensus.

Table 2: Distribution of images per public source and category in SIMAS dataset




Type
Category
LAION
Roboflow
NudeNet
Kaggle
Unsplash
UnsafeBench
Other
Total




Unsafe
Alcohol
29
0
3
267
0
1
0
300


Unsafe
Drugs
17
211
0
0
13
8
1
250


Unsafe
Firearms
0
59
0
229
0
62
0
350


Unsafe
Gambling
132
38
0
0
73
39
18
300


Unsafe
Sexual
2
0
421
0
3
68
6
500


Unsafe
Tobacco
0
446
0
0
43
11
0
500


Unsafe
Violence
0
289
0
0
0
11
0
300


Safe
Alcohol
140
35
0
0
16
13
96
300


Safe
Drugs
67
49
0
15
72
17
30
250


Safe
Firearms
173
15
0
3
144
8
7
350


Safe
Gambling
164
2
0
1
121
12
0
300


Safe
Sexual
235
22
139
2
0
94
8
500


Safe
Tobacco
351
67
5
13
8
16
40
500


Safe
Violence
212
20
3
21
0
42
2
300


All
All
1,522
1,253
571
551
493
402
208
5,000




Balancing

To ensure semantic diversity and dataset balance, undersampling was performed on overrepresented categories using a CLIP-based embedding and k-means clustering strategy. This resulted in a final dataset containing 2,500 spam and 2,500 safe images, evenly distributed across all categories.

Table 3: Distribution of images per category in SIMAS dataset




Type
Alcohol
Drugs
Firearms
Gambling
Sexual
Tobacco
Violence
Total




Unsafe
300
250
350
300
500
500
300
2,500


Safe
300
250
350
300
500
500
300
2,500


All
600
500
700
600
1,000
1,000
600
5,000




SIMAS+ Dataset



For researchers interested in a more realistic deployment setting, we also curate a complementary dataset called SIMAS+. It is a benchmarking dataset containing publicly accessible images extracted from real-world MMS traffic, specifically from external URLs embedded in messages. Manual annotation was conducted by three independent raters, with a category label assigned when at least two annotators agreed. The dataset was then balanced across spam categories using the same semantic grouping strategy as in SIMAS, ensuring equal representation of safe and unsafe examples per class. The final version of SIMAS+ contains 700 images, with the category distribution presented in the table below.

 

Table 4: Distribution of images per category in SIMAS+ dataset





Type
Alcohol
Drugs
Firearms
Gambling
Sexual
Tobacco
Violence
Total




Unsafe
100
50
80
50
50
10
10
350


Safe
100
50
80
50
50
10
10
350


All
200
100
160
100
100
20
20
700




 



Note: Due to regulatory and privacy considerations, SIMAS+ is not included in this archive. To obtain access to the SIMAS+ dataset for research purposes, please contact the dataset authors directly.

 



License
This dataset is licensed under the CC BY-NC 4.0 license and may be used for non-commercial research purposes.